AI assisted Geofence generation from aerial imagery : Open Vocabulary Object Detection and Zero-shot image segmentation for mobility

Sammanfattning

This thesis explores the application of artificial intelligence for automatic geofence generation from satellite imagery. Geofences—virtual perimeters for monitoring vehicle movements—are essential for fleet management but currently require manual configuration, a process that is time-consuming, error-prone, and labor-intensive, especially for companies like Volvo that manage thousands of locations worldwide. Creating a system that can automatically detect and define appropriate geofence boundaries presents significant challenges: property boundaries lack universal visual patterns, industrial areas have complex layouts, and different facilities require context-specific boundary definitions. Previous approaches have struggled because property lines often have no distinct visual signatures in satellite imagery, making traditional heuristic algorithms ineffective. This research develops an AI-based pipeline that integrates Open-StreetMap vector data with high-resolution satellite imagery from TomTom. The system employs a sequential approach using foundation models: first applying GroundingDINO (a zero-shot object detector) with text prompts to identify areas of interest, then using Segment Anything Model 2 (SAM2) to generate precise masks for industrial features. The pipeline follows a two-pass strategy—first filtering out vegetation, then identifying industrial elements like buildings, asphalt, parking lots, and vehicles—to construct geofence polygons around facilities. Testing with approximately 1,500 global commercial locations revealed that the system achieved a 30.4% perfect detection rate on first attempts, with an additional 5.9% correct on second attempts. Even imperfect predictions were valuable: 43.7% captured the target areas but were slightly larger than optimal, while 19.8% identified core facility features but missed peripheral areas. These results demonstrate that non-fine-tuned AI foundation models can generate reasonable geofences automatically without requiring labeled training data. This approach offers significant time savings for transportation companies and fleet managers who currently draw thousands of boundaries manually, while providing a foundation for more sophisticated geofencing systems that could improve vehicle safety, efficiency, and environmental compliance.