Definition
Geospatial annotation is the process of labeling geographic data such as satellite images, aerial photos, or LiDAR scans with meaningful tags like roads, buildings, or vegetation.
Purpose
The purpose is to create structured datasets for training geospatial AI systems. It supports applications in mapping, urban planning, agriculture, and disaster response.
Importance
- Essential for building accurate geographic information systems (GIS).
- Supports environmental monitoring and smart city projects.
- Raises privacy and security concerns in surveillance.
- Requires precision due to real-world safety implications.
How It Works
- Collect satellite or aerial imagery.
- Define annotation categories (roads, water, crops, etc.).
- Use tools to draw polygons, bounding boxes, or segmentation masks.
- Validate annotations against ground truth data.
- Train and deploy geospatial AI systems.
Examples (Real World)
- Google Maps: annotated satellite imagery for navigation.
- Planet Labs: annotated Earth imagery for agriculture and climate.
- UNOSAT (United Nations): annotated imagery for disaster relief.
References / Further Reading
- ISO/TC 211: Geographic Information Standards.
- “Deep Learning in Remote Sensing” — IEEE Geoscience and Remote Sensing Magazine.
- USGS Earth Explorer — United States Geological Survey.