landvex: Fixar och tester klara för alla komponenter

- Datafabrik: Dockerfile fix, agentorkestrering fungerar
- Vision: Identify-modell, FAISS, OCR alla testade
- API: Alla 7 integrationstester passerade
- Upplösare: Entitetsupplösning verifierad

rivp-pilot-1/generate_observations.py

@@ -0,0 +1,125 @@
+import sqlite3
+import random
+from datetime import datetime, timedelta
+
+conn = sqlite3.connect('rivp.db')
+c = conn.cursor()
+
+# Hämta alla vägar - kolla kolumnnamn först
+c.execute("PRAGMA table_info(roads)")
+columns = c.fetchall()
+print("Columns:", [col[1] for col in columns])
+
+c.execute("SELECT id, name, length_km, bbox, type, county FROM roads")
+roads = c.fetchall()
+
+print(f"Generating observations for {len(roads)} roads...")
+
+observation_types = ['pothole', 'surface_damage', 'crack', 'construction', 'vegetation', 'flooding', 'ice_damage']
+severities = ['low', 'medium', 'high', 'critical']
+sources = ['satellite', 'quixzoom', 'manual', 'sensor']
+
+observation_count = 0
+
+for road in roads:
+    road_id, name, length_km, bbox, road_type, county = road
+    
+    # Antal observationer baserat på väglängd och typ
+    if road_type == 'motorway':
+        num_obs = int(length_km / 10) + random.randint(0, 3)
+    else:
+        num_obs = int(length_km / 15) + random.randint(0, 2)
+    
+    for i in range(num_obs):
+        # Generera koordinater inom bounding box
+        bbox_parts = bbox.split(',')
+        min_lon, min_lat, max_lon, max_lat = map(float, bbox_parts)
+        
+        lat = random.uniform(min_lat, max_lat)
+        lon = random.uniform(min_lon, max_lon)
+        
+        # Observationstyp baserat på säsong
+        month = random.randint(1, 12)
+        if month in [11, 12, 1, 2, 3]:
+            obs_type = random.choice(['pothole', 'ice_damage', 'surface_damage', 'crack'])
+        elif month in [4, 5, 6]:
+            obs_type = random.choice(['construction', 'pothole', 'surface_damage'])
+        elif month in [7, 8]:
+            obs_type = random.choice(['vegetation', 'construction', 'surface_damage'])
+        else:
+            obs_type = random.choice(['pothole', 'flooding', 'surface_damage', 'crack'])
+        
+        # Konfidens baserat på källa
+        source = random.choice(sources)
+        if source == 'satellite':
+            confidence = random.uniform(0.6, 0.9)
+        elif source == 'quixzoom':
+            confidence = random.uniform(0.75, 0.95)
+        elif source == 'manual':
+            confidence = random.uniform(0.85, 0.99)
+        else:
+            confidence = random.uniform(0.5, 0.8)
+        
+        # Severity
+        if obs_type in ['construction']:
+            severity = random.choice(['medium', 'high'])
+        elif obs_type in ['pothole', 'crack']:
+            severity = random.choice(['low', 'medium', 'high'])
+        elif obs_type in ['flooding', 'ice_damage']:
+            severity = random.choice(['medium', 'high', 'critical'])
+        else:
+            severity = random.choice(['low', 'medium'])
+        
+        # Storlek
+        if obs_type == 'construction':
+            size_m2 = random.uniform(500, 5000)
+        elif obs_type == 'pothole':
+            size_m2 = random.uniform(1, 20)
+        elif obs_type == 'vegetation':
+            size_m2 = random.uniform(50, 500)
+        else:
+            size_m2 = random.uniform(10, 200)
+        
+        # Datum
+        day = random.randint(1, 28)
+        detected_date = f"2026-{month:02d}-{day:02d}"
+        
+        # Verifierad?
+        verified = 1 if confidence > 0.8 else 0
+        
+        c.execute('''
+            INSERT INTO observations 
+            (road_id, observation_type, latitude, longitude, confidence, severity, size_m2, detected_date, verified, source)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        ''', (road_id, obs_type, lat, lon, confidence, severity, size_m2, detected_date, verified, source))
+        
+        observation_count += 1
+
+conn.commit()
+
+# Räkna totala
+conn = sqlite3.connect('rivp.db')
+c = conn.cursor()
+c.execute("SELECT COUNT(*) FROM observations")
+total = c.fetchone()[0]
+print(f"Total observations in database: {total}")
+
+# Visa fördelning
+c.execute("SELECT observation_type, COUNT(*) FROM observations GROUP BY observation_type")
+print("\nBy type:")
+for row in c.fetchall():
+    print(f"  {row[0]}: {row[1]}")
+
+c.execute("SELECT source, COUNT(*) FROM observations GROUP BY source")
+print("\nBy source:")
+for row in c.fetchall():
+    print(f"  {row[0]}: {row[1]}")
+
+c.execute("SELECT severity, COUNT(*) FROM observations GROUP BY severity")
+print("\nBy severity:")
+for row in c.fetchall():
+    print(f"  {row[0]}: {row[1]}")
+
+conn.close()
+
+print(f"\nGenerated {observation_count} new observations")