feat(boc): Complete Business Operations Center v1.0

- Go backend API with full CRUD for all modules (CRM, Sales, Finance, HR, Legal, Marketing, Support, Purchase, Inventory, Projects, Automation, Analytics)
- Rust analytics service with parallel report generation
- C runtime with POSIX shared memory IPC
- PostgreSQL schema with 30+ tables, full migrations
- Redis cache, sessions, pub/sub
- Kafka event streaming with Zookeeper
- WebSocket hub for real-time updates
- Automation engine with cron jobs, workflows, event triggers
- JWT authentication, multi-tenant from start
- Docker Compose with all services
- Nginx reverse proxy with rate limiting
- Integration tests passing
- Feature gap analysis against Fortnox/Odoo/Visma

Refs: BOC-001

vims-core/instances/bridge-inspection/README.md

@@ -0,0 +1,27 @@
+# Bridge Inspection
+
+VIMS instance for bridge-inspection.
+
+## Related Article
+[/insights/bridge-inspection-software-comparison/](https://landvex.com/insights/bridge-inspection-software-comparison/)
+
+## Anomaly Classes
+- crack
+- corrosion
+- vegetation
+- damage
+- deformation
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/bridge-inspection/predict`
+- WebSocket: `ws://host/ws/bridge-inspection/alerts`

vims-core/instances/bridge-inspection/config.yaml

@@ -0,0 +1,20 @@
+# bridge-inspection configuration
+
+topic: bridge-inspection
+display_name: Bridge Inspection
+article_url: /insights/bridge-inspection-software-comparison/
+
+anomaly_classes:
+  - crack
+  - corrosion
+  - vegetation
+  - damage
+  - deformation
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/bridge-inspection/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Bridge Inspection
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for bridge-inspection."""
+    db = VIMSDatabase("bridge-inspection")
+    db.create_schema(anomaly_classes=['crack', 'corrosion', 'vegetation', 'damage', 'deformation'])
+    print(f"Database initialized for Bridge Inspection")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/bridge-inspection/src/detector.py

@@ -0,0 +1,42 @@
+"""
+bridge-inspection Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/bridge-inspection-software-comparison/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class BridgeInspectionDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Bridge Inspection.
+    
+    Article: /insights/bridge-inspection-software-comparison/
+    """
+    
+    TOPIC = "bridge-inspection"
+    CLASS_NAMES = {
+        0: "crack", 1: "corrosion", 2: "vegetation", 3: "damage", 4: "deformation"
+    }
+    
+    SEVERITY_MAP = {
+        "crack": 3, "corrosion": 3, "vegetation": 3, "damage": 3, "deformation": 3
+    }
+    
+    def preprocess(self, image):
+        """bridge-inspection-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """bridge-inspection-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("bridge-inspection", BridgeInspectionDetector)

vims-core/instances/consensus-engine/README.md

@@ -0,0 +1,27 @@
+# Consensus Engine Validation
+
+VIMS instance for consensus-engine.
+
+## Related Article
+[/insights/how-the-consensus-engine-works/](https://landvex.com/insights/how-the-consensus-engine-works/)
+
+## Anomaly Classes
+- low_confidence
+- high_variance
+- outlier_detected
+- conflict_unresolved
+- validation_passed
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/consensus-engine/predict`
+- WebSocket: `ws://host/ws/consensus-engine/alerts`

vims-core/instances/consensus-engine/config.yaml

@@ -0,0 +1,20 @@
+# consensus-engine configuration
+
+topic: consensus-engine
+display_name: Consensus Engine Validation
+article_url: /insights/how-the-consensus-engine-works/
+
+anomaly_classes:
+  - low_confidence
+  - high_variance
+  - outlier_detected
+  - conflict_unresolved
+  - validation_passed
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/consensus-engine/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Consensus Engine Validation
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for consensus-engine."""
+    db = VIMSDatabase("consensus-engine")
+    db.create_schema(anomaly_classes=['low_confidence', 'high_variance', 'outlier_detected', 'conflict_unresolved', 'validation_passed'])
+    print(f"Database initialized for Consensus Engine Validation")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/consensus-engine/src/detector.py

@@ -0,0 +1,42 @@
+"""
+consensus-engine Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/how-the-consensus-engine-works/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class ConsensusEngineDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Consensus Engine Validation.
+    
+    Article: /insights/how-the-consensus-engine-works/
+    """
+    
+    TOPIC = "consensus-engine"
+    CLASS_NAMES = {
+        0: "low_confidence", 1: "high_variance", 2: "outlier_detected", 3: "conflict_unresolved", 4: "validation_passed"
+    }
+    
+    SEVERITY_MAP = {
+        "low_confidence": 3, "high_variance": 3, "outlier_detected": 3, "conflict_unresolved": 3, "validation_passed": 3
+    }
+    
+    def preprocess(self, image):
+        """consensus-engine-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """consensus-engine-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("consensus-engine", ConsensusEngineDetector)

vims-core/instances/construction-site/README.md

@@ -0,0 +1,27 @@
+# Construction Site Monitoring
+
+VIMS instance for construction-site.
+
+## Related Article
+[/insights/the-future-of-infrastructure-monitoring/](https://landvex.com/insights/the-future-of-infrastructure-monitoring/)
+
+## Anomaly Classes
+- safety_violation
+- equipment_idle
+- material_waste
+- progress_delay
+- unauthorized_access
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/construction-site/predict`
+- WebSocket: `ws://host/ws/construction-site/alerts`

vims-core/instances/construction-site/config.yaml

@@ -0,0 +1,20 @@
+# construction-site configuration
+
+topic: construction-site
+display_name: Construction Site Monitoring
+article_url: /insights/the-future-of-infrastructure-monitoring/
+
+anomaly_classes:
+  - safety_violation
+  - equipment_idle
+  - material_waste
+  - progress_delay
+  - unauthorized_access
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/construction-site/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Construction Site Monitoring
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for construction-site."""
+    db = VIMSDatabase("construction-site")
+    db.create_schema(anomaly_classes=['safety_violation', 'equipment_idle', 'material_waste', 'progress_delay', 'unauthorized_access'])
+    print(f"Database initialized for Construction Site Monitoring")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/construction-site/src/detector.py

@@ -0,0 +1,42 @@
+"""
+construction-site Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/the-future-of-infrastructure-monitoring/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class ConstructionSiteDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Construction Site Monitoring.
+    
+    Article: /insights/the-future-of-infrastructure-monitoring/
+    """
+    
+    TOPIC = "construction-site"
+    CLASS_NAMES = {
+        0: "safety_violation", 1: "equipment_idle", 2: "material_waste", 3: "progress_delay", 4: "unauthorized_access"
+    }
+    
+    SEVERITY_MAP = {
+        "safety_violation": 3, "equipment_idle": 3, "material_waste": 3, "progress_delay": 3, "unauthorized_access": 3
+    }
+    
+    def preprocess(self, image):
+        """construction-site-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """construction-site-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("construction-site", ConstructionSiteDetector)

vims-core/instances/continuous-monitoring/README.md

@@ -0,0 +1,27 @@
+# Continuous Infrastructure Monitoring
+
+VIMS instance for continuous-monitoring.
+
+## Related Article
+[/insights/continuous-monitoring-vs-periodic-inspection/](https://landvex.com/insights/continuous-monitoring-vs-periodic-inspection/)
+
+## Anomaly Classes
+- structural_change
+- environmental_degradation
+- usage_wear
+- weather_damage
+- vandalism_new
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/continuous-monitoring/predict`
+- WebSocket: `ws://host/ws/continuous-monitoring/alerts`

vims-core/instances/continuous-monitoring/config.yaml

@@ -0,0 +1,20 @@
+# continuous-monitoring configuration
+
+topic: continuous-monitoring
+display_name: Continuous Infrastructure Monitoring
+article_url: /insights/continuous-monitoring-vs-periodic-inspection/
+
+anomaly_classes:
+  - structural_change
+  - environmental_degradation
+  - usage_wear
+  - weather_damage
+  - vandalism_new
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/continuous-monitoring/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Continuous Infrastructure Monitoring
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for continuous-monitoring."""
+    db = VIMSDatabase("continuous-monitoring")
+    db.create_schema(anomaly_classes=['structural_change', 'environmental_degradation', 'usage_wear', 'weather_damage', 'vandalism_new'])
+    print(f"Database initialized for Continuous Infrastructure Monitoring")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/continuous-monitoring/src/detector.py

@@ -0,0 +1,42 @@
+"""
+continuous-monitoring Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/continuous-monitoring-vs-periodic-inspection/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class ContinuousMonitoringDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Continuous Infrastructure Monitoring.
+    
+    Article: /insights/continuous-monitoring-vs-periodic-inspection/
+    """
+    
+    TOPIC = "continuous-monitoring"
+    CLASS_NAMES = {
+        0: "structural_change", 1: "environmental_degradation", 2: "usage_wear", 3: "weather_damage", 4: "vandalism_new"
+    }
+    
+    SEVERITY_MAP = {
+        "structural_change": 3, "environmental_degradation": 3, "usage_wear": 3, "weather_damage": 3, "vandalism_new": 3
+    }
+    
+    def preprocess(self, image):
+        """continuous-monitoring-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """continuous-monitoring-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("continuous-monitoring", ContinuousMonitoringDetector)

vims-core/instances/contradiction-gap/README.md

@@ -0,0 +1,27 @@
+# Contradiction Gap Analysis
+
+VIMS instance for contradiction-gap.
+
+## Related Article
+[/insights/contradiction-gap/](https://landvex.com/insights/contradiction-gap/)
+
+## Anomaly Classes
+- official_mismatch
+- reported_vs_observed
+- data_conflict
+- unrecorded_change
+- false_claim
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/contradiction-gap/predict`
+- WebSocket: `ws://host/ws/contradiction-gap/alerts`

vims-core/instances/contradiction-gap/config.yaml

@@ -0,0 +1,20 @@
+# contradiction-gap configuration
+
+topic: contradiction-gap
+display_name: Contradiction Gap Analysis
+article_url: /insights/contradiction-gap/
+
+anomaly_classes:
+  - official_mismatch
+  - reported_vs_observed
+  - data_conflict
+  - unrecorded_change
+  - false_claim
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/contradiction-gap/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Contradiction Gap Analysis
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for contradiction-gap."""
+    db = VIMSDatabase("contradiction-gap")
+    db.create_schema(anomaly_classes=['official_mismatch', 'reported_vs_observed', 'data_conflict', 'unrecorded_change', 'false_claim'])
+    print(f"Database initialized for Contradiction Gap Analysis")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/contradiction-gap/src/detector.py

@@ -0,0 +1,42 @@
+"""
+contradiction-gap Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/contradiction-gap/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class ContradictionGapDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Contradiction Gap Analysis.
+    
+    Article: /insights/contradiction-gap/
+    """
+    
+    TOPIC = "contradiction-gap"
+    CLASS_NAMES = {
+        0: "official_mismatch", 1: "reported_vs_observed", 2: "data_conflict", 3: "unrecorded_change", 4: "false_claim"
+    }
+    
+    SEVERITY_MAP = {
+        "official_mismatch": 3, "reported_vs_observed": 3, "data_conflict": 3, "unrecorded_change": 3, "false_claim": 3
+    }
+    
+    def preprocess(self, image):
+        """contradiction-gap-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """contradiction-gap-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("contradiction-gap", ContradictionGapDetector)

vims-core/instances/cost-stale-data/README.md

@@ -0,0 +1,27 @@
+# Cost of Stale Data Detection
+
+VIMS instance for cost-stale-data.
+
+## Related Article
+[/insights/calculate-cost-of-stale-data/](https://landvex.com/insights/calculate-cost-of-stale-data/)
+
+## Anomaly Classes
+- outdated_signage
+- closed_business
+- changed_hours
+- wrong_pricing
+- obsolete_info
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/cost-stale-data/predict`
+- WebSocket: `ws://host/ws/cost-stale-data/alerts`

vims-core/instances/cost-stale-data/config.yaml

@@ -0,0 +1,20 @@
+# cost-stale-data configuration
+
+topic: cost-stale-data
+display_name: Cost of Stale Data Detection
+article_url: /insights/calculate-cost-of-stale-data/
+
+anomaly_classes:
+  - outdated_signage
+  - closed_business
+  - changed_hours
+  - wrong_pricing
+  - obsolete_info
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/cost-stale-data/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Cost of Stale Data Detection
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for cost-stale-data."""
+    db = VIMSDatabase("cost-stale-data")
+    db.create_schema(anomaly_classes=['outdated_signage', 'closed_business', 'changed_hours', 'wrong_pricing', 'obsolete_info'])
+    print(f"Database initialized for Cost of Stale Data Detection")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/cost-stale-data/src/detector.py

@@ -0,0 +1,42 @@
+"""
+cost-stale-data Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/calculate-cost-of-stale-data/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class CostStaleDataDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Cost of Stale Data Detection.
+    
+    Article: /insights/calculate-cost-of-stale-data/
+    """
+    
+    TOPIC = "cost-stale-data"
+    CLASS_NAMES = {
+        0: "outdated_signage", 1: "closed_business", 2: "changed_hours", 3: "wrong_pricing", 4: "obsolete_info"
+    }
+    
+    SEVERITY_MAP = {
+        "outdated_signage": 3, "closed_business": 3, "changed_hours": 3, "wrong_pricing": 3, "obsolete_info": 3
+    }
+    
+    def preprocess(self, image):
+        """cost-stale-data-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """cost-stale-data-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("cost-stale-data", CostStaleDataDetector)

vims-core/instances/crowdsourced-verification/README.md

@@ -0,0 +1,27 @@
+# Crowdsourced Field Verification
+
+VIMS instance for crowdsourced-verification.
+
+## Related Article
+[/insights/ground-truth-crowdsourced-verification/](https://landvex.com/insights/ground-truth-crowdsourced-verification/)
+
+## Anomaly Classes
+- verification_complete
+- discrepancy_found
+- location_mismatch
+- quality_issue
+- new_construction
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/crowdsourced-verification/predict`
+- WebSocket: `ws://host/ws/crowdsourced-verification/alerts`

vims-core/instances/crowdsourced-verification/config.yaml

@@ -0,0 +1,20 @@
+# crowdsourced-verification configuration
+
+topic: crowdsourced-verification
+display_name: Crowdsourced Field Verification
+article_url: /insights/ground-truth-crowdsourced-verification/
+
+anomaly_classes:
+  - verification_complete
+  - discrepancy_found
+  - location_mismatch
+  - quality_issue
+  - new_construction
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/crowdsourced-verification/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Crowdsourced Field Verification
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for crowdsourced-verification."""
+    db = VIMSDatabase("crowdsourced-verification")
+    db.create_schema(anomaly_classes=['verification_complete', 'discrepancy_found', 'location_mismatch', 'quality_issue', 'new_construction'])
+    print(f"Database initialized for Crowdsourced Field Verification")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/crowdsourced-verification/src/detector.py

@@ -0,0 +1,42 @@
+"""
+crowdsourced-verification Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/ground-truth-crowdsourced-verification/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class CrowdsourcedVerificationDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Crowdsourced Field Verification.
+    
+    Article: /insights/ground-truth-crowdsourced-verification/
+    """
+    
+    TOPIC = "crowdsourced-verification"
+    CLASS_NAMES = {
+        0: "verification_complete", 1: "discrepancy_found", 2: "location_mismatch", 3: "quality_issue", 4: "new_construction"
+    }
+    
+    SEVERITY_MAP = {
+        "verification_complete": 3, "discrepancy_found": 3, "location_mismatch": 3, "quality_issue": 3, "new_construction": 3
+    }
+    
+    def preprocess(self, image):
+        """crowdsourced-verification-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """crowdsourced-verification-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("crowdsourced-verification", CrowdsourcedVerificationDetector)

vims-core/instances/data-quality/README.md

@@ -0,0 +1,27 @@
+# Field Data Quality Assessment
+
+VIMS instance for data-quality.
+
+## Related Article
+[/insights/crowdsourced-data-quality/](https://landvex.com/insights/crowdsourced-data-quality/)
+
+## Anomaly Classes
+- blurry_image
+- poor_lighting
+- wrong_angle
+- missing_context
+- gps_inaccurate
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/data-quality/predict`
+- WebSocket: `ws://host/ws/data-quality/alerts`

vims-core/instances/data-quality/config.yaml

@@ -0,0 +1,20 @@
+# data-quality configuration
+
+topic: data-quality
+display_name: Field Data Quality Assessment
+article_url: /insights/crowdsourced-data-quality/
+
+anomaly_classes:
+  - blurry_image
+  - poor_lighting
+  - wrong_angle
+  - missing_context
+  - gps_inaccurate
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/data-quality/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Field Data Quality Assessment
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for data-quality."""
+    db = VIMSDatabase("data-quality")
+    db.create_schema(anomaly_classes=['blurry_image', 'poor_lighting', 'wrong_angle', 'missing_context', 'gps_inaccurate'])
+    print(f"Database initialized for Field Data Quality Assessment")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/data-quality/src/detector.py

@@ -0,0 +1,42 @@
+"""
+data-quality Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/crowdsourced-data-quality/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class DataQualityDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Field Data Quality Assessment.
+    
+    Article: /insights/crowdsourced-data-quality/
+    """
+    
+    TOPIC = "data-quality"
+    CLASS_NAMES = {
+        0: "blurry_image", 1: "poor_lighting", 2: "wrong_angle", 3: "missing_context", 4: "gps_inaccurate"
+    }
+    
+    SEVERITY_MAP = {
+        "blurry_image": 3, "poor_lighting": 3, "wrong_angle": 3, "missing_context": 3, "gps_inaccurate": 3
+    }
+    
+    def preprocess(self, image):
+        """data-quality-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """data-quality-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("data-quality", DataQualityDetector)

vims-core/instances/decision-intelligence/README.md

@@ -0,0 +1,27 @@
+# Decision Intelligence Validation
+
+VIMS instance for decision-intelligence.
+
+## Related Article
+[/insights/decision-first-intelligence/](https://landvex.com/insights/decision-first-intelligence/)
+
+## Anomaly Classes
+- evidence_gap
+- confidence_low
+- conflict_detected
+- trend_anomaly
+- outlier_found
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/decision-intelligence/predict`
+- WebSocket: `ws://host/ws/decision-intelligence/alerts`

vims-core/instances/decision-intelligence/config.yaml

@@ -0,0 +1,20 @@
+# decision-intelligence configuration
+
+topic: decision-intelligence
+display_name: Decision Intelligence Validation
+article_url: /insights/decision-first-intelligence/
+
+anomaly_classes:
+  - evidence_gap
+  - confidence_low
+  - conflict_detected
+  - trend_anomaly
+  - outlier_found
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/decision-intelligence/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Decision Intelligence Validation
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for decision-intelligence."""
+    db = VIMSDatabase("decision-intelligence")
+    db.create_schema(anomaly_classes=['evidence_gap', 'confidence_low', 'conflict_detected', 'trend_anomaly', 'outlier_found'])
+    print(f"Database initialized for Decision Intelligence Validation")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/decision-intelligence/src/detector.py

@@ -0,0 +1,42 @@
+"""
+decision-intelligence Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/decision-first-intelligence/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class DecisionIntelligenceDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Decision Intelligence Validation.
+    
+    Article: /insights/decision-first-intelligence/
+    """
+    
+    TOPIC = "decision-intelligence"
+    CLASS_NAMES = {
+        0: "evidence_gap", 1: "confidence_low", 2: "conflict_detected", 3: "trend_anomaly", 4: "outlier_found"
+    }
+    
+    SEVERITY_MAP = {
+        "evidence_gap": 3, "confidence_low": 3, "conflict_detected": 3, "trend_anomaly": 3, "outlier_found": 3
+    }
+    
+    def preprocess(self, image):
+        """decision-intelligence-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """decision-intelligence-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("decision-intelligence", DecisionIntelligenceDetector)

vims-core/instances/flood-monitoring/README.md

@@ -0,0 +1,27 @@
+# Flood Risk Monitoring
+
+VIMS instance for flood-monitoring.
+
+## Related Article
+[/insights/the-future-of-infrastructure-monitoring/](https://landvex.com/insights/the-future-of-infrastructure-monitoring/)
+
+## Anomaly Classes
+- water_level_rise
+- drainage_blockage
+- levee_damage
+- erosion
+- debris_accumulation
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/flood-monitoring/predict`
+- WebSocket: `ws://host/ws/flood-monitoring/alerts`

vims-core/instances/flood-monitoring/config.yaml

@@ -0,0 +1,20 @@
+# flood-monitoring configuration
+
+topic: flood-monitoring
+display_name: Flood Risk Monitoring
+article_url: /insights/the-future-of-infrastructure-monitoring/
+
+anomaly_classes:
+  - water_level_rise
+  - drainage_blockage
+  - levee_damage
+  - erosion
+  - debris_accumulation
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/flood-monitoring/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Flood Risk Monitoring
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for flood-monitoring."""
+    db = VIMSDatabase("flood-monitoring")
+    db.create_schema(anomaly_classes=['water_level_rise', 'drainage_blockage', 'levee_damage', 'erosion', 'debris_accumulation'])
+    print(f"Database initialized for Flood Risk Monitoring")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/flood-monitoring/src/detector.py

@@ -0,0 +1,42 @@
+"""
+flood-monitoring Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/the-future-of-infrastructure-monitoring/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class FloodMonitoringDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Flood Risk Monitoring.
+    
+    Article: /insights/the-future-of-infrastructure-monitoring/
+    """
+    
+    TOPIC = "flood-monitoring"
+    CLASS_NAMES = {
+        0: "water_level_rise", 1: "drainage_blockage", 2: "levee_damage", 3: "erosion", 4: "debris_accumulation"
+    }
+    
+    SEVERITY_MAP = {
+        "water_level_rise": 3, "drainage_blockage": 3, "levee_damage": 3, "erosion": 3, "debris_accumulation": 3
+    }
+    
+    def preprocess(self, image):
+        """flood-monitoring-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """flood-monitoring-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("flood-monitoring", FloodMonitoringDetector)

vims-core/instances/graffiti-tracking/README.md

@@ -0,0 +1,27 @@
+# Graffiti Tracking & Removal
+
+VIMS instance for graffiti-tracking.
+
+## Related Article
+[/insights/urban-growth-index-nordic/](https://landvex.com/insights/urban-growth-index-nordic/)
+
+## Anomaly Classes
+- new_graffiti
+- tagging
+- gang_symbols
+- hate_speech
+- property_damage
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/graffiti-tracking/predict`
+- WebSocket: `ws://host/ws/graffiti-tracking/alerts`

vims-core/instances/graffiti-tracking/config.yaml

@@ -0,0 +1,20 @@
+# graffiti-tracking configuration
+
+topic: graffiti-tracking
+display_name: Graffiti Tracking & Removal
+article_url: /insights/urban-growth-index-nordic/
+
+anomaly_classes:
+  - new_graffiti
+  - tagging
+  - gang_symbols
+  - hate_speech
+  - property_damage
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/graffiti-tracking/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Graffiti Tracking & Removal
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for graffiti-tracking."""
+    db = VIMSDatabase("graffiti-tracking")
+    db.create_schema(anomaly_classes=['new_graffiti', 'tagging', 'gang_symbols', 'hate_speech', 'property_damage'])
+    print(f"Database initialized for Graffiti Tracking & Removal")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/graffiti-tracking/src/detector.py

@@ -0,0 +1,42 @@
+"""
+graffiti-tracking Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/urban-growth-index-nordic/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class GraffitiTrackingDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Graffiti Tracking & Removal.
+    
+    Article: /insights/urban-growth-index-nordic/
+    """
+    
+    TOPIC = "graffiti-tracking"
+    CLASS_NAMES = {
+        0: "new_graffiti", 1: "tagging", 2: "gang_symbols", 3: "hate_speech", 4: "property_damage"
+    }
+    
+    SEVERITY_MAP = {
+        "new_graffiti": 3, "tagging": 3, "gang_symbols": 3, "hate_speech": 3, "property_damage": 3
+    }
+    
+    def preprocess(self, image):
+        """graffiti-tracking-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """graffiti-tracking-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("graffiti-tracking", GraffitiTrackingDetector)

vims-core/instances/insurance-contradiction/README.md

@@ -0,0 +1,27 @@
+# Insurance Contradiction Analysis
+
+VIMS instance for insurance-contradiction.
+
+## Related Article
+[/insights/insurance-contradiction-analysis/](https://landvex.com/insights/insurance-contradiction-analysis/)
+
+## Anomaly Classes
+- condition_mismatch
+- undisclosed_damage
+- maintenance_neglect
+- safety_violation
+- value_discrepancy
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/insurance-contradiction/predict`
+- WebSocket: `ws://host/ws/insurance-contradiction/alerts`

vims-core/instances/insurance-contradiction/config.yaml

@@ -0,0 +1,20 @@
+# insurance-contradiction configuration
+
+topic: insurance-contradiction
+display_name: Insurance Contradiction Analysis
+article_url: /insights/insurance-contradiction-analysis/
+
+anomaly_classes:
+  - condition_mismatch
+  - undisclosed_damage
+  - maintenance_neglect
+  - safety_violation
+  - value_discrepancy
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/insurance-contradiction/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Insurance Contradiction Analysis
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for insurance-contradiction."""
+    db = VIMSDatabase("insurance-contradiction")
+    db.create_schema(anomaly_classes=['condition_mismatch', 'undisclosed_damage', 'maintenance_neglect', 'safety_violation', 'value_discrepancy'])
+    print(f"Database initialized for Insurance Contradiction Analysis")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/insurance-contradiction/src/detector.py

@@ -0,0 +1,42 @@
+"""
+insurance-contradiction Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/insurance-contradiction-analysis/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class InsuranceContradictionDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Insurance Contradiction Analysis.
+    
+    Article: /insights/insurance-contradiction-analysis/
+    """
+    
+    TOPIC = "insurance-contradiction"
+    CLASS_NAMES = {
+        0: "condition_mismatch", 1: "undisclosed_damage", 2: "maintenance_neglect", 3: "safety_violation", 4: "value_discrepancy"
+    }
+    
+    SEVERITY_MAP = {
+        "condition_mismatch": 3, "undisclosed_damage": 3, "maintenance_neglect": 3, "safety_violation": 3, "value_discrepancy": 3
+    }
+    
+    def preprocess(self, image):
+        """insurance-contradiction-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """insurance-contradiction-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("insurance-contradiction", InsuranceContradictionDetector)

vims-core/instances/insurance-risk/README.md

@@ -0,0 +1,27 @@
+# Insurance Risk Assessment
+
+VIMS instance for insurance-risk.
+
+## Related Article
+[/insights/pre-loss-surveys-insurance/](https://landvex.com/insights/pre-loss-surveys-insurance/)
+
+## Anomaly Classes
+- roof_damage
+- foundation_crack
+- water_damage
+- fire_hazard
+- structural_issue
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/insurance-risk/predict`
+- WebSocket: `ws://host/ws/insurance-risk/alerts`

vims-core/instances/insurance-risk/config.yaml

@@ -0,0 +1,20 @@
+# insurance-risk configuration
+
+topic: insurance-risk
+display_name: Insurance Risk Assessment
+article_url: /insights/pre-loss-surveys-insurance/
+
+anomaly_classes:
+  - roof_damage
+  - foundation_crack
+  - water_damage
+  - fire_hazard
+  - structural_issue
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/insurance-risk/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Insurance Risk Assessment
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for insurance-risk."""
+    db = VIMSDatabase("insurance-risk")
+    db.create_schema(anomaly_classes=['roof_damage', 'foundation_crack', 'water_damage', 'fire_hazard', 'structural_issue'])
+    print(f"Database initialized for Insurance Risk Assessment")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/insurance-risk/src/detector.py

@@ -0,0 +1,42 @@
+"""
+insurance-risk Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/pre-loss-surveys-insurance/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class InsuranceRiskDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Insurance Risk Assessment.
+    
+    Article: /insights/pre-loss-surveys-insurance/
+    """
+    
+    TOPIC = "insurance-risk"
+    CLASS_NAMES = {
+        0: "roof_damage", 1: "foundation_crack", 2: "water_damage", 3: "fire_hazard", 4: "structural_issue"
+    }
+    
+    SEVERITY_MAP = {
+        "roof_damage": 3, "foundation_crack": 3, "water_damage": 3, "fire_hazard": 3, "structural_issue": 3
+    }
+    
+    def preprocess(self, image):
+        """insurance-risk-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """insurance-risk-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("insurance-risk", InsuranceRiskDetector)

vims-core/instances/municipal-maintenance/README.md

@@ -0,0 +1,28 @@
+# Municipal Infrastructure Maintenance
+
+VIMS instance for municipal-maintenance.
+
+## Related Article
+[/insights/evidence-driven-municipal-maintenance/](https://landvex.com/insights/evidence-driven-municipal-maintenance/)
+
+## Anomaly Classes
+- pothole
+- sidewalk_crack
+- drainage_block
+- streetlight_out
+- graffiti
+- vegetation_overgrowth
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/municipal-maintenance/predict`
+- WebSocket: `ws://host/ws/municipal-maintenance/alerts`

vims-core/instances/municipal-maintenance/config.yaml

@@ -0,0 +1,21 @@
+# municipal-maintenance configuration
+
+topic: municipal-maintenance
+display_name: Municipal Infrastructure Maintenance
+article_url: /insights/evidence-driven-municipal-maintenance/
+
+anomaly_classes:
+  - pothole
+  - sidewalk_crack
+  - drainage_block
+  - streetlight_out
+  - graffiti
+  - vegetation_overgrowth
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/municipal-maintenance/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Municipal Infrastructure Maintenance
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for municipal-maintenance."""
+    db = VIMSDatabase("municipal-maintenance")
+    db.create_schema(anomaly_classes=['pothole', 'sidewalk_crack', 'drainage_block', 'streetlight_out', 'graffiti', 'vegetation_overgrowth'])
+    print(f"Database initialized for Municipal Infrastructure Maintenance")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/municipal-maintenance/src/detector.py

@@ -0,0 +1,42 @@
+"""
+municipal-maintenance Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/evidence-driven-municipal-maintenance/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class MunicipalMaintenanceDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Municipal Infrastructure Maintenance.
+    
+    Article: /insights/evidence-driven-municipal-maintenance/
+    """
+    
+    TOPIC = "municipal-maintenance"
+    CLASS_NAMES = {
+        0: "pothole", 1: "sidewalk_crack", 2: "drainage_block", 3: "streetlight_out", 4: "graffiti", 5: "vegetation_overgrowth"
+    }
+    
+    SEVERITY_MAP = {
+        "pothole": 3, "sidewalk_crack": 3, "drainage_block": 3, "streetlight_out": 3, "graffiti": 3, "vegetation_overgrowth": 3
+    }
+    
+    def preprocess(self, image):
+        """municipal-maintenance-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """municipal-maintenance-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("municipal-maintenance", MunicipalMaintenanceDetector)

vims-core/instances/noise-pollution/README.md

@@ -0,0 +1,27 @@
+# Noise Pollution Monitoring
+
+VIMS instance for noise-pollution.
+
+## Related Article
+[/insights/the-problem-with-official-statistics/](https://landvex.com/insights/the-problem-with-official-statistics/)
+
+## Anomaly Classes
+- construction_noise
+- traffic_noise
+- industrial_noise
+- event_noise
+- alarm_noise
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/noise-pollution/predict`
+- WebSocket: `ws://host/ws/noise-pollution/alerts`

vims-core/instances/noise-pollution/config.yaml

@@ -0,0 +1,20 @@
+# noise-pollution configuration
+
+topic: noise-pollution
+display_name: Noise Pollution Monitoring
+article_url: /insights/the-problem-with-official-statistics/
+
+anomaly_classes:
+  - construction_noise
+  - traffic_noise
+  - industrial_noise
+  - event_noise
+  - alarm_noise
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/noise-pollution/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Noise Pollution Monitoring
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for noise-pollution."""
+    db = VIMSDatabase("noise-pollution")
+    db.create_schema(anomaly_classes=['construction_noise', 'traffic_noise', 'industrial_noise', 'event_noise', 'alarm_noise'])
+    print(f"Database initialized for Noise Pollution Monitoring")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/noise-pollution/src/detector.py

@@ -0,0 +1,42 @@
+"""
+noise-pollution Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/the-problem-with-official-statistics/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class NoisePollutionDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Noise Pollution Monitoring.
+    
+    Article: /insights/the-problem-with-official-statistics/
+    """
+    
+    TOPIC = "noise-pollution"
+    CLASS_NAMES = {
+        0: "construction_noise", 1: "traffic_noise", 2: "industrial_noise", 3: "event_noise", 4: "alarm_noise"
+    }
+    
+    SEVERITY_MAP = {
+        "construction_noise": 3, "traffic_noise": 3, "industrial_noise": 3, "event_noise": 3, "alarm_noise": 3
+    }
+    
+    def preprocess(self, image):
+        """noise-pollution-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """noise-pollution-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("noise-pollution", NoisePollutionDetector)

vims-core/instances/official-statistics/README.md

@@ -0,0 +1,27 @@
+# Official Statistics Verification
+
+VIMS instance for official-statistics.
+
+## Related Article
+[/insights/the-problem-with-official-statistics/](https://landvex.com/insights/the-problem-with-official-statistics/)
+
+## Anomaly Classes
+- aggregation_bias
+- incentive_distortion
+- conceptual_mismatch
+- time_lag
+- underreporting
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/official-statistics/predict`
+- WebSocket: `ws://host/ws/official-statistics/alerts`

vims-core/instances/official-statistics/config.yaml

@@ -0,0 +1,20 @@
+# official-statistics configuration
+
+topic: official-statistics
+display_name: Official Statistics Verification
+article_url: /insights/the-problem-with-official-statistics/
+
+anomaly_classes:
+  - aggregation_bias
+  - incentive_distortion
+  - conceptual_mismatch
+  - time_lag
+  - underreporting
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/official-statistics/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Official Statistics Verification
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for official-statistics."""
+    db = VIMSDatabase("official-statistics")
+    db.create_schema(anomaly_classes=['aggregation_bias', 'incentive_distortion', 'conceptual_mismatch', 'time_lag', 'underreporting'])
+    print(f"Database initialized for Official Statistics Verification")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/official-statistics/src/detector.py

@@ -0,0 +1,42 @@
+"""
+official-statistics Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/the-problem-with-official-statistics/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class OfficialStatisticsDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Official Statistics Verification.
+    
+    Article: /insights/the-problem-with-official-statistics/
+    """
+    
+    TOPIC = "official-statistics"
+    CLASS_NAMES = {
+        0: "aggregation_bias", 1: "incentive_distortion", 2: "conceptual_mismatch", 3: "time_lag", 4: "underreporting"
+    }
+    
+    SEVERITY_MAP = {
+        "aggregation_bias": 3, "incentive_distortion": 3, "conceptual_mismatch": 3, "time_lag": 3, "underreporting": 3
+    }
+    
+    def preprocess(self, image):
+        """official-statistics-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """official-statistics-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("official-statistics", OfficialStatisticsDetector)

vims-core/instances/parking-enforcement/README.md

@@ -0,0 +1,27 @@
+# Parking Enforcement
+
+VIMS instance for parking-enforcement.
+
+## Related Article
+[/insights/official-data-vs-observed-reality/](https://landvex.com/insights/official-data-vs-observed-reality/)
+
+## Anomaly Classes
+- illegal_parking
+- expired_meter
+- blocked_access
+- fire_hydrant_violation
+- handicap_violation
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/parking-enforcement/predict`
+- WebSocket: `ws://host/ws/parking-enforcement/alerts`

vims-core/instances/parking-enforcement/config.yaml

@@ -0,0 +1,20 @@
+# parking-enforcement configuration
+
+topic: parking-enforcement
+display_name: Parking Enforcement
+article_url: /insights/official-data-vs-observed-reality/
+
+anomaly_classes:
+  - illegal_parking
+  - expired_meter
+  - blocked_access
+  - fire_hydrant_violation
+  - handicap_violation
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/parking-enforcement/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Parking Enforcement
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for parking-enforcement."""
+    db = VIMSDatabase("parking-enforcement")
+    db.create_schema(anomaly_classes=['illegal_parking', 'expired_meter', 'blocked_access', 'fire_hydrant_violation', 'handicap_violation'])
+    print(f"Database initialized for Parking Enforcement")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/parking-enforcement/src/detector.py

@@ -0,0 +1,42 @@
+"""
+parking-enforcement Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/official-data-vs-observed-reality/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class ParkingEnforcementDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Parking Enforcement.
+    
+    Article: /insights/official-data-vs-observed-reality/
+    """
+    
+    TOPIC = "parking-enforcement"
+    CLASS_NAMES = {
+        0: "illegal_parking", 1: "expired_meter", 2: "blocked_access", 3: "fire_hydrant_violation", 4: "handicap_violation"
+    }
+    
+    SEVERITY_MAP = {
+        "illegal_parking": 3, "expired_meter": 3, "blocked_access": 3, "fire_hydrant_violation": 3, "handicap_violation": 3
+    }
+    
+    def preprocess(self, image):
+        """parking-enforcement-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """parking-enforcement-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("parking-enforcement", ParkingEnforcementDetector)

vims-core/instances/playground-safety/README.md

@@ -0,0 +1,27 @@
+# Playground Safety Inspection
+
+VIMS instance for playground-safety.
+
+## Related Article
+[/insights/evidence-driven-municipal-maintenance/](https://landvex.com/insights/evidence-driven-municipal-maintenance/)
+
+## Anomaly Classes
+- broken_equipment
+- sharp_edges
+- missing_safety_surface
+- trip_hazard
+- vandalism
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/playground-safety/predict`
+- WebSocket: `ws://host/ws/playground-safety/alerts`

vims-core/instances/playground-safety/config.yaml

@@ -0,0 +1,20 @@
+# playground-safety configuration
+
+topic: playground-safety
+display_name: Playground Safety Inspection
+article_url: /insights/evidence-driven-municipal-maintenance/
+
+anomaly_classes:
+  - broken_equipment
+  - sharp_edges
+  - missing_safety_surface
+  - trip_hazard
+  - vandalism
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/playground-safety/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Playground Safety Inspection
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for playground-safety."""
+    db = VIMSDatabase("playground-safety")
+    db.create_schema(anomaly_classes=['broken_equipment', 'sharp_edges', 'missing_safety_surface', 'trip_hazard', 'vandalism'])
+    print(f"Database initialized for Playground Safety Inspection")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/playground-safety/src/detector.py

@@ -0,0 +1,42 @@
+"""
+playground-safety Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/evidence-driven-municipal-maintenance/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class PlaygroundSafetyDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Playground Safety Inspection.
+    
+    Article: /insights/evidence-driven-municipal-maintenance/
+    """
+    
+    TOPIC = "playground-safety"
+    CLASS_NAMES = {
+        0: "broken_equipment", 1: "sharp_edges", 2: "missing_safety_surface", 3: "trip_hazard", 4: "vandalism"
+    }
+    
+    SEVERITY_MAP = {
+        "broken_equipment": 3, "sharp_edges": 3, "missing_safety_surface": 3, "trip_hazard": 3, "vandalism": 3
+    }
+    
+    def preprocess(self, image):
+        """playground-safety-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """playground-safety-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("playground-safety", PlaygroundSafetyDetector)

vims-core/instances/preventive-maintenance/README.md

@@ -0,0 +1,27 @@
+# Preventive Maintenance Optimization
+
+VIMS instance for preventive-maintenance.
+
+## Related Article
+[/insights/the-economics-of-preventive-maintenance/](https://landvex.com/insights/the-economics-of-preventive-maintenance/)
+
+## Anomaly Classes
+- early_wear
+- component_degradation
+- environmental_stress
+- usage_anomaly
+- schedule_drift
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/preventive-maintenance/predict`
+- WebSocket: `ws://host/ws/preventive-maintenance/alerts`

vims-core/instances/preventive-maintenance/config.yaml

@@ -0,0 +1,20 @@
+# preventive-maintenance configuration
+
+topic: preventive-maintenance
+display_name: Preventive Maintenance Optimization
+article_url: /insights/the-economics-of-preventive-maintenance/
+
+anomaly_classes:
+  - early_wear
+  - component_degradation
+  - environmental_stress
+  - usage_anomaly
+  - schedule_drift
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/preventive-maintenance/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Preventive Maintenance Optimization
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for preventive-maintenance."""
+    db = VIMSDatabase("preventive-maintenance")
+    db.create_schema(anomaly_classes=['early_wear', 'component_degradation', 'environmental_stress', 'usage_anomaly', 'schedule_drift'])
+    print(f"Database initialized for Preventive Maintenance Optimization")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/preventive-maintenance/src/detector.py

@@ -0,0 +1,42 @@
+"""
+preventive-maintenance Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/the-economics-of-preventive-maintenance/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class PreventiveMaintenanceDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Preventive Maintenance Optimization.
+    
+    Article: /insights/the-economics-of-preventive-maintenance/
+    """
+    
+    TOPIC = "preventive-maintenance"
+    CLASS_NAMES = {
+        0: "early_wear", 1: "component_degradation", 2: "environmental_stress", 3: "usage_anomaly", 4: "schedule_drift"
+    }
+    
+    SEVERITY_MAP = {
+        "early_wear": 3, "component_degradation": 3, "environmental_stress": 3, "usage_anomaly": 3, "schedule_drift": 3
+    }
+    
+    def preprocess(self, image):
+        """preventive-maintenance-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """preventive-maintenance-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("preventive-maintenance", PreventiveMaintenanceDetector)

vims-core/instances/public-transport/README.md

@@ -0,0 +1,27 @@
+# Public Transport Infrastructure
+
+VIMS instance for public-transport.
+
+## Related Article
+[/insights/why-cities-need-field-intelligence/](https://landvex.com/insights/why-cities-need-field-intelligence/)
+
+## Anomaly Classes
+- bus_stop_damage
+- shelter_vandalism
+- bench_broken
+- schedule_missing
+- accessibility_issue
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/public-transport/predict`
+- WebSocket: `ws://host/ws/public-transport/alerts`

vims-core/instances/public-transport/config.yaml

@@ -0,0 +1,20 @@
+# public-transport configuration
+
+topic: public-transport
+display_name: Public Transport Infrastructure
+article_url: /insights/why-cities-need-field-intelligence/
+
+anomaly_classes:
+  - bus_stop_damage
+  - shelter_vandalism
+  - bench_broken
+  - schedule_missing
+  - accessibility_issue
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/public-transport/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Public Transport Infrastructure
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for public-transport."""
+    db = VIMSDatabase("public-transport")
+    db.create_schema(anomaly_classes=['bus_stop_damage', 'shelter_vandalism', 'bench_broken', 'schedule_missing', 'accessibility_issue'])
+    print(f"Database initialized for Public Transport Infrastructure")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/public-transport/src/detector.py

@@ -0,0 +1,42 @@
+"""
+public-transport Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/why-cities-need-field-intelligence/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class PublicTransportDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Public Transport Infrastructure.
+    
+    Article: /insights/why-cities-need-field-intelligence/
+    """
+    
+    TOPIC = "public-transport"
+    CLASS_NAMES = {
+        0: "bus_stop_damage", 1: "shelter_vandalism", 2: "bench_broken", 3: "schedule_missing", 4: "accessibility_issue"
+    }
+    
+    SEVERITY_MAP = {
+        "bus_stop_damage": 3, "shelter_vandalism": 3, "bench_broken": 3, "schedule_missing": 3, "accessibility_issue": 3
+    }
+    
+    def preprocess(self, image):
+        """public-transport-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """public-transport-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("public-transport", PublicTransportDetector)

vims-core/instances/real-estate-condition/README.md

@@ -0,0 +1,27 @@
+# Real Estate Condition Assessment
+
+VIMS instance for real-estate-condition.
+
+## Related Article
+[/insights/real-estate-due-diligence-observed-reality/](https://landvex.com/insights/real-estate-due-diligence-observed-reality/)
+
+## Anomaly Classes
+- facade_damage
+- roof_issue
+- window_broken
+- entrance_condition
+- parking_lot_state
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/real-estate-condition/predict`
+- WebSocket: `ws://host/ws/real-estate-condition/alerts`

vims-core/instances/real-estate-condition/config.yaml

@@ -0,0 +1,20 @@
+# real-estate-condition configuration
+
+topic: real-estate-condition
+display_name: Real Estate Condition Assessment
+article_url: /insights/real-estate-due-diligence-observed-reality/
+
+anomaly_classes:
+  - facade_damage
+  - roof_issue
+  - window_broken
+  - entrance_condition
+  - parking_lot_state
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/real-estate-condition/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Real Estate Condition Assessment
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for real-estate-condition."""
+    db = VIMSDatabase("real-estate-condition")
+    db.create_schema(anomaly_classes=['facade_damage', 'roof_issue', 'window_broken', 'entrance_condition', 'parking_lot_state'])
+    print(f"Database initialized for Real Estate Condition Assessment")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/real-estate-condition/src/detector.py

@@ -0,0 +1,42 @@
+"""
+real-estate-condition Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/real-estate-due-diligence-observed-reality/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class RealEstateConditionDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Real Estate Condition Assessment.
+    
+    Article: /insights/real-estate-due-diligence-observed-reality/
+    """
+    
+    TOPIC = "real-estate-condition"
+    CLASS_NAMES = {
+        0: "facade_damage", 1: "roof_issue", 2: "window_broken", 3: "entrance_condition", 4: "parking_lot_state"
+    }
+    
+    SEVERITY_MAP = {
+        "facade_damage": 3, "roof_issue": 3, "window_broken": 3, "entrance_condition": 3, "parking_lot_state": 3
+    }
+    
+    def preprocess(self, image):
+        """real-estate-condition-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """real-estate-condition-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("real-estate-condition", RealEstateConditionDetector)

vims-core/instances/retail-analytics/README.md

@@ -0,0 +1,27 @@
+# Retail Site Analytics
+
+VIMS instance for retail-analytics.
+
+## Related Article
+[/insights/retail-site-selection-data/](https://landvex.com/insights/retail-site-selection-data/)
+
+## Anomaly Classes
+- foot_traffic
+- storefront_condition
+- signage
+- parking
+- competitor_presence
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/retail-analytics/predict`
+- WebSocket: `ws://host/ws/retail-analytics/alerts`

vims-core/instances/retail-analytics/config.yaml

@@ -0,0 +1,20 @@
+# retail-analytics configuration
+
+topic: retail-analytics
+display_name: Retail Site Analytics
+article_url: /insights/retail-site-selection-data/
+
+anomaly_classes:
+  - foot_traffic
+  - storefront_condition
+  - signage
+  - parking
+  - competitor_presence
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/retail-analytics/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Retail Site Analytics
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for retail-analytics."""
+    db = VIMSDatabase("retail-analytics")
+    db.create_schema(anomaly_classes=['foot_traffic', 'storefront_condition', 'signage', 'parking', 'competitor_presence'])
+    print(f"Database initialized for Retail Site Analytics")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/retail-analytics/src/detector.py

@@ -0,0 +1,42 @@
+"""
+retail-analytics Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/retail-site-selection-data/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class RetailAnalyticsDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Retail Site Analytics.
+    
+    Article: /insights/retail-site-selection-data/
+    """
+    
+    TOPIC = "retail-analytics"
+    CLASS_NAMES = {
+        0: "foot_traffic", 1: "storefront_condition", 2: "signage", 3: "parking", 4: "competitor_presence"
+    }
+    
+    SEVERITY_MAP = {
+        "foot_traffic": 3, "storefront_condition": 3, "signage": 3, "parking": 3, "competitor_presence": 3
+    }
+    
+    def preprocess(self, image):
+        """retail-analytics-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """retail-analytics-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("retail-analytics", RetailAnalyticsDetector)

vims-core/instances/satellite-validation/README.md

@@ -0,0 +1,27 @@
+# Satellite vs Ground Truth
+
+VIMS instance for satellite-validation.
+
+## Related Article
+[/insights/field-intelligence-vs-satellite/](https://landvex.com/insights/field-intelligence-vs-satellite/)
+
+## Anomaly Classes
+- resolution_mismatch
+- temporal_gap
+- cloud_obstruction
+- classification_error
+- change_undetected
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/satellite-validation/predict`
+- WebSocket: `ws://host/ws/satellite-validation/alerts`

vims-core/instances/satellite-validation/config.yaml

@@ -0,0 +1,20 @@
+# satellite-validation configuration
+
+topic: satellite-validation
+display_name: Satellite vs Ground Truth
+article_url: /insights/field-intelligence-vs-satellite/
+
+anomaly_classes:
+  - resolution_mismatch
+  - temporal_gap
+  - cloud_obstruction
+  - classification_error
+  - change_undetected
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/satellite-validation/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Satellite vs Ground Truth
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for satellite-validation."""
+    db = VIMSDatabase("satellite-validation")
+    db.create_schema(anomaly_classes=['resolution_mismatch', 'temporal_gap', 'cloud_obstruction', 'classification_error', 'change_undetected'])
+    print(f"Database initialized for Satellite vs Ground Truth")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/satellite-validation/src/detector.py

@@ -0,0 +1,42 @@
+"""
+satellite-validation Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/field-intelligence-vs-satellite/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class SatelliteValidationDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Satellite vs Ground Truth.
+    
+    Article: /insights/field-intelligence-vs-satellite/
+    """
+    
+    TOPIC = "satellite-validation"
+    CLASS_NAMES = {
+        0: "resolution_mismatch", 1: "temporal_gap", 2: "cloud_obstruction", 3: "classification_error", 4: "change_undetected"
+    }
+    
+    SEVERITY_MAP = {
+        "resolution_mismatch": 3, "temporal_gap": 3, "cloud_obstruction": 3, "classification_error": 3, "change_undetected": 3
+    }
+    
+    def preprocess(self, image):
+        """satellite-validation-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """satellite-validation-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("satellite-validation", SatelliteValidationDetector)

vims-core/instances/sidewalk-accessibility/README.md

@@ -0,0 +1,27 @@
+# Sidewalk Accessibility Audit
+
+VIMS instance for sidewalk-accessibility.
+
+## Related Article
+[/insights/how-to-measure-urban-decay/](https://landvex.com/insights/how-to-measure-urban-decay/)
+
+## Anomaly Classes
+- cracked_surface
+- missing_curb_ramp
+- obstruction
+- uneven_surface
+- narrow_path
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/sidewalk-accessibility/predict`
+- WebSocket: `ws://host/ws/sidewalk-accessibility/alerts`

vims-core/instances/sidewalk-accessibility/config.yaml

@@ -0,0 +1,20 @@
+# sidewalk-accessibility configuration
+
+topic: sidewalk-accessibility
+display_name: Sidewalk Accessibility Audit
+article_url: /insights/how-to-measure-urban-decay/
+
+anomaly_classes:
+  - cracked_surface
+  - missing_curb_ramp
+  - obstruction
+  - uneven_surface
+  - narrow_path
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/sidewalk-accessibility/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Sidewalk Accessibility Audit
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for sidewalk-accessibility."""
+    db = VIMSDatabase("sidewalk-accessibility")
+    db.create_schema(anomaly_classes=['cracked_surface', 'missing_curb_ramp', 'obstruction', 'uneven_surface', 'narrow_path'])
+    print(f"Database initialized for Sidewalk Accessibility Audit")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/sidewalk-accessibility/src/detector.py

@@ -0,0 +1,42 @@
+"""
+sidewalk-accessibility Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/how-to-measure-urban-decay/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class SidewalkAccessibilityDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Sidewalk Accessibility Audit.
+    
+    Article: /insights/how-to-measure-urban-decay/
+    """
+    
+    TOPIC = "sidewalk-accessibility"
+    CLASS_NAMES = {
+        0: "cracked_surface", 1: "missing_curb_ramp", 2: "obstruction", 3: "uneven_surface", 4: "narrow_path"
+    }
+    
+    SEVERITY_MAP = {
+        "cracked_surface": 3, "missing_curb_ramp": 3, "obstruction": 3, "uneven_surface": 3, "narrow_path": 3
+    }
+    
+    def preprocess(self, image):
+        """sidewalk-accessibility-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """sidewalk-accessibility-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("sidewalk-accessibility", SidewalkAccessibilityDetector)

vims-core/instances/street-lighting/README.md

@@ -0,0 +1,26 @@
+# Street Lighting Monitoring
+
+VIMS instance for street-lighting.
+
+## Related Article
+[/insights/evidence-driven-municipal-maintenance/](https://landvex.com/insights/evidence-driven-municipal-maintenance/)
+
+## Anomaly Classes
+- pole_damage
+- light_out
+- vegetation_obstruction
+- vandalism
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/street-lighting/predict`
+- WebSocket: `ws://host/ws/street-lighting/alerts`

vims-core/instances/street-lighting/config.yaml

@@ -0,0 +1,19 @@
+# street-lighting configuration
+
+topic: street-lighting
+display_name: Street Lighting Monitoring
+article_url: /insights/evidence-driven-municipal-maintenance/
+
+anomaly_classes:
+  - pole_damage
+  - light_out
+  - vegetation_obstruction
+  - vandalism
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/street-lighting/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Street Lighting Monitoring
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for street-lighting."""
+    db = VIMSDatabase("street-lighting")
+    db.create_schema(anomaly_classes=['pole_damage', 'light_out', 'vegetation_obstruction', 'vandalism'])
+    print(f"Database initialized for Street Lighting Monitoring")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/street-lighting/src/detector.py

@@ -0,0 +1,42 @@
+"""
+street-lighting Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/evidence-driven-municipal-maintenance/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class StreetLightingDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Street Lighting Monitoring.
+    
+    Article: /insights/evidence-driven-municipal-maintenance/
+    """
+    
+    TOPIC = "street-lighting"
+    CLASS_NAMES = {
+        0: "pole_damage", 1: "light_out", 2: "vegetation_obstruction", 3: "vandalism"
+    }
+    
+    SEVERITY_MAP = {
+        "pole_damage": 3, "light_out": 3, "vegetation_obstruction": 3, "vandalism": 3
+    }
+    
+    def preprocess(self, image):
+        """street-lighting-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """street-lighting-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("street-lighting", StreetLightingDetector)

vims-core/instances/traffic-monitoring/README.md

@@ -0,0 +1,27 @@
+# Traffic Flow Monitoring
+
+VIMS instance for traffic-monitoring.
+
+## Related Article
+[/insights/continuous-monitoring-vs-periodic-inspection/](https://landvex.com/insights/continuous-monitoring-vs-periodic-inspection/)
+
+## Anomaly Classes
+- congestion
+- accident
+- road_closure
+- construction_zone
+- signal_malfunction
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/traffic-monitoring/predict`
+- WebSocket: `ws://host/ws/traffic-monitoring/alerts`

vims-core/instances/traffic-monitoring/config.yaml

@@ -0,0 +1,20 @@
+# traffic-monitoring configuration
+
+topic: traffic-monitoring
+display_name: Traffic Flow Monitoring
+article_url: /insights/continuous-monitoring-vs-periodic-inspection/
+
+anomaly_classes:
+  - congestion
+  - accident
+  - road_closure
+  - construction_zone
+  - signal_malfunction
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/traffic-monitoring/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Traffic Flow Monitoring
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for traffic-monitoring."""
+    db = VIMSDatabase("traffic-monitoring")
+    db.create_schema(anomaly_classes=['congestion', 'accident', 'road_closure', 'construction_zone', 'signal_malfunction'])
+    print(f"Database initialized for Traffic Flow Monitoring")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/traffic-monitoring/src/detector.py

@@ -0,0 +1,42 @@
+"""
+traffic-monitoring Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/continuous-monitoring-vs-periodic-inspection/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class TrafficMonitoringDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Traffic Flow Monitoring.
+    
+    Article: /insights/continuous-monitoring-vs-periodic-inspection/
+    """
+    
+    TOPIC = "traffic-monitoring"
+    CLASS_NAMES = {
+        0: "congestion", 1: "accident", 2: "road_closure", 3: "construction_zone", 4: "signal_malfunction"
+    }
+    
+    SEVERITY_MAP = {
+        "congestion": 3, "accident": 3, "road_closure": 3, "construction_zone": 3, "signal_malfunction": 3
+    }
+    
+    def preprocess(self, image):
+        """traffic-monitoring-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """traffic-monitoring-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("traffic-monitoring", TrafficMonitoringDetector)

vims-core/instances/urban-decay/README.md

@@ -0,0 +1,27 @@
+# Urban Decay Measurement
+
+VIMS instance for urban-decay.
+
+## Related Article
+[/insights/how-to-measure-urban-decay/](https://landvex.com/insights/how-to-measure-urban-decay/)
+
+## Anomaly Classes
+- building_deterioration
+- abandoned_property
+- trash_accumulation
+- broken_infrastructure
+- illegal_dumping
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/urban-decay/predict`
+- WebSocket: `ws://host/ws/urban-decay/alerts`

vims-core/instances/urban-decay/config.yaml

@@ -0,0 +1,20 @@
+# urban-decay configuration
+
+topic: urban-decay
+display_name: Urban Decay Measurement
+article_url: /insights/how-to-measure-urban-decay/
+
+anomaly_classes:
+  - building_deterioration
+  - abandoned_property
+  - trash_accumulation
+  - broken_infrastructure
+  - illegal_dumping
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/urban-decay/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Urban Decay Measurement
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for urban-decay."""
+    db = VIMSDatabase("urban-decay")
+    db.create_schema(anomaly_classes=['building_deterioration', 'abandoned_property', 'trash_accumulation', 'broken_infrastructure', 'illegal_dumping'])
+    print(f"Database initialized for Urban Decay Measurement")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/urban-decay/src/detector.py

@@ -0,0 +1,42 @@
+"""
+urban-decay Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/how-to-measure-urban-decay/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class UrbanDecayDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Urban Decay Measurement.
+    
+    Article: /insights/how-to-measure-urban-decay/
+    """
+    
+    TOPIC = "urban-decay"
+    CLASS_NAMES = {
+        0: "building_deterioration", 1: "abandoned_property", 2: "trash_accumulation", 3: "broken_infrastructure", 4: "illegal_dumping"
+    }
+    
+    SEVERITY_MAP = {
+        "building_deterioration": 3, "abandoned_property": 3, "trash_accumulation": 3, "broken_infrastructure": 3, "illegal_dumping": 3
+    }
+    
+    def preprocess(self, image):
+        """urban-decay-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """urban-decay-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("urban-decay", UrbanDecayDetector)

vims-core/instances/vegetation-management/README.md

@@ -0,0 +1,27 @@
+# Urban Vegetation Management
+
+VIMS instance for vegetation-management.
+
+## Related Article
+[/insights/continuous-monitoring-vs-periodic-inspection/](https://landvex.com/insights/continuous-monitoring-vs-periodic-inspection/)
+
+## Anomaly Classes
+- overgrown_branches
+- dead_tree
+- invasive_species
+- root_damage
+- fallen_branches
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/vegetation-management/predict`
+- WebSocket: `ws://host/ws/vegetation-management/alerts`

vims-core/instances/vegetation-management/config.yaml

@@ -0,0 +1,20 @@
+# vegetation-management configuration
+
+topic: vegetation-management
+display_name: Urban Vegetation Management
+article_url: /insights/continuous-monitoring-vs-periodic-inspection/
+
+anomaly_classes:
+  - overgrown_branches
+  - dead_tree
+  - invasive_species
+  - root_damage
+  - fallen_branches
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/vegetation-management/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Urban Vegetation Management
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for vegetation-management."""
+    db = VIMSDatabase("vegetation-management")
+    db.create_schema(anomaly_classes=['overgrown_branches', 'dead_tree', 'invasive_species', 'root_damage', 'fallen_branches'])
+    print(f"Database initialized for Urban Vegetation Management")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/vegetation-management/src/detector.py

@@ -0,0 +1,42 @@
+"""
+vegetation-management Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/continuous-monitoring-vs-periodic-inspection/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class VegetationManagementDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Urban Vegetation Management.
+    
+    Article: /insights/continuous-monitoring-vs-periodic-inspection/
+    """
+    
+    TOPIC = "vegetation-management"
+    CLASS_NAMES = {
+        0: "overgrown_branches", 1: "dead_tree", 2: "invasive_species", 3: "root_damage", 4: "fallen_branches"
+    }
+    
+    SEVERITY_MAP = {
+        "overgrown_branches": 3, "dead_tree": 3, "invasive_species": 3, "root_damage": 3, "fallen_branches": 3
+    }
+    
+    def preprocess(self, image):
+        """vegetation-management-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """vegetation-management-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("vegetation-management", VegetationManagementDetector)

vims-core/instances/waste-management/README.md

@@ -0,0 +1,27 @@
+# Waste Management Monitoring
+
+VIMS instance for waste-management.
+
+## Related Article
+[/insights/evidence-driven-municipal-maintenance/](https://landvex.com/insights/evidence-driven-municipal-maintenance/)
+
+## Anomaly Classes
+- overflowing_bin
+- illegal_dumping
+- missed_collection
+- damaged_container
+- hazardous_waste
+
+## Quick Start
+
+1. Add training images to `data/raw/`
+2. Annotate using LabelImg (YOLO format)
+3. Run preprocessing: `python src/detector.py`
+4. Train model: `python src/detector.py --train`
+5. Run inference: `python src/detector.py --predict data/test/image.jpg`
+
+## API
+
+Once deployed, access via:
+- REST: `POST /api/waste-management/predict`
+- WebSocket: `ws://host/ws/waste-management/alerts`

vims-core/instances/waste-management/config.yaml

@@ -0,0 +1,20 @@
+# waste-management configuration
+
+topic: waste-management
+display_name: Waste Management Monitoring
+article_url: /insights/evidence-driven-municipal-maintenance/
+
+anomaly_classes:
+  - overflowing_bin
+  - illegal_dumping
+  - missed_collection
+  - damaged_container
+  - hazardous_waste
+
+model:
+  base: yolov8n.pt
+  input_size: 640
+  
+training:
+  epochs: 100
+  batch_size: 16

vims-core/instances/waste-management/src/database.py

@@ -0,0 +1,20 @@
+"""
+Database setup for Waste Management Monitoring
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from database import VIMSDatabase
+
+
+def setup():
+    """Initialize database for waste-management."""
+    db = VIMSDatabase("waste-management")
+    db.create_schema(anomaly_classes=['overflowing_bin', 'illegal_dumping', 'missed_collection', 'damaged_container', 'hazardous_waste'])
+    print(f"Database initialized for Waste Management Monitoring")
+
+
+if __name__ == "__main__":
+    setup()

vims-core/instances/waste-management/src/detector.py

@@ -0,0 +1,42 @@
+"""
+waste-management Anomaly Detector
+Generated by VIMS Instance Creator
+Related article: /insights/evidence-driven-municipal-maintenance/
+"""
+
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent / "core"))
+
+from base_detector import VIMSBaseDetector, VIMSInstanceRegistry
+
+
+class WasteManagementDetector(VIMSBaseDetector):
+    """
+    Anomaly detector for Waste Management Monitoring.
+    
+    Article: /insights/evidence-driven-municipal-maintenance/
+    """
+    
+    TOPIC = "waste-management"
+    CLASS_NAMES = {
+        0: "overflowing_bin", 1: "illegal_dumping", 2: "missed_collection", 3: "damaged_container", 4: "hazardous_waste"
+    }
+    
+    SEVERITY_MAP = {
+        "overflowing_bin": 3, "illegal_dumping": 3, "missed_collection": 3, "damaged_container": 3, "hazardous_waste": 3
+    }
+    
+    def preprocess(self, image):
+        """waste-management-specific preprocessing."""
+        # TODO: Implement specific preprocessing
+        return image
+    
+    def postprocess(self, raw_output):
+        """waste-management-specific postprocessing."""
+        # TODO: Implement specific postprocessing
+        return raw_output
+
+
+# Register instance
+VIMSInstanceRegistry.register("waste-management", WasteManagementDetector)