AI-Powered System for Detecting Empty Land Plots in Spain + Catastro API Integration
Budget: $3,000 – $5,000 USD
I need an experienced developer or team to build an AI-powered automated system that can:
⸻
1. Use AI / Computer Vision to detect empty land plots
The system should use AI / machine learning / computer vision to analyze satellite imagery (Google Maps, Google Earth, or other sources) and automatically identify:
• empty land plots
• undeveloped parcels in urban areas
• gaps between houses
• areas with no built structures
The AI should generate for each detected plot:
• GPS coordinates
• polygon boundaries
• estimated area
• classification confidence
(If needed, OpenStreetMap building footprints can be combined for better accuracy.)
⸻
2. Retrieve cadastral data using Catastro Spain API
Once a plot is detected by the AI, the system must query the official Catastro API (WMS/WFS/REST) and extract:
• Cadastral Reference (REF Catastral)
• official area (m²)
• land classification
• polygon boundaries
• coordinates
• any additional publicly available cadastral information
⸻
3. AI-assisted visualization dashboard
The system must include a web-based dashboard with:
• interactive map (Leaflet / Mapbox / Google Maps)
• AI-detected plots displayed as polygons
• click to view cadastral information
• filters by size, class, confidence level
• option to run AI analysis for a selected town or drawn area
⸻
4. Report generation
The system must produce:
• CSV/Excel file with full parcel data
• GeoJSON export
• optional PDF report with:
• map snapshots
• detected parcels
• basic statistics
• AI confidence scores
⸻
5. Technical requirements
• Python-based backend
• AI/ML or Computer Vision (TensorFlow, PyTorch, YOLO, Detectron, etc.)
• GIS libraries (geopandas, shapely, rasterio)
• Google Maps / OSM integration
• Catastro API integration
⸻
1. Use AI / Computer Vision to detect empty land plots
The system should use AI / machine learning / computer vision to analyze satellite imagery (Google Maps, Google Earth, or other sources) and automatically identify:
• empty land plots
• undeveloped parcels in urban areas
• gaps between houses
• areas with no built structures
The AI should generate for each detected plot:
• GPS coordinates
• polygon boundaries
• estimated area
• classification confidence
(If needed, OpenStreetMap building footprints can be combined for better accuracy.)
⸻
2. Retrieve cadastral data using Catastro Spain API
Once a plot is detected by the AI, the system must query the official Catastro API (WMS/WFS/REST) and extract:
• Cadastral Reference (REF Catastral)
• official area (m²)
• land classification
• polygon boundaries
• coordinates
• any additional publicly available cadastral information
⸻
3. AI-assisted visualization dashboard
The system must include a web-based dashboard with:
• interactive map (Leaflet / Mapbox / Google Maps)
• AI-detected plots displayed as polygons
• click to view cadastral information
• filters by size, class, confidence level
• option to run AI analysis for a selected town or drawn area
⸻
4. Report generation
The system must produce:
• CSV/Excel file with full parcel data
• GeoJSON export
• optional PDF report with:
• map snapshots
• detected parcels
• basic statistics
• AI confidence scores
⸻
5. Technical requirements
• Python-based backend
• AI/ML or Computer Vision (TensorFlow, PyTorch, YOLO, Detectron, etc.)
• GIS libraries (geopandas, shapely, rasterio)
• Google Maps / OSM integration
• Catastro API integration