GeoAI - GeoBase Series: Canadian Geospatial Features Extracted from Satellite Imagery

GeoAI - GeoBase Series is a collection of foundational geospatial data for Canada, including buildings, hydrography, forests, and roads. The data is automatically extracted using Deep Learning models applied to aerial or satellite imagery by Natural Resources Canada. The series is designed to increase the availability of high-resolution geospatial data and is published on the open_canada platform.

Use Cases

• Change detection analysis for urban development based on building footprints.
• Monitoring forest cover and deforestation over time.
• Mapping and analyzing road network infrastructure.
• Hydrographic modeling and water resource management based on extracted water features.
• Creating foundational map layers for regional planning and analysis.

Strengths

• Data is created using leading-edge Artificial Intelligence models, enabling a rapid and scalable process.
• The series includes multiple Canadian cities (e.g., Iqaluit, Calgary, Québec City) as demonstrated by provided use case links.
• Data is available in multiple standard geospatial file formats including SHP, GPKG, and ESRI REST.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic bias inherent to the selected Canadian cities.

Provenance

Source

Natural Resources Canada

Collection Method

Automatically extracted using Deep Learning models applied to aerial or satellite imagery.

Freshness

Last updated 2026-07-03 17:01:40.494668; freshness should be verified.

Geography

Canada, with specific use cases for cities including Iqaluit, Calgary, Québec City, Winnipeg, Victoria, and Trois-Rivières.