GeoAI
A time-enabled topographic feature series.
We’ve got some exciting news!
The latest update to the GeoAI Data Series adds over 275,000 km² of new AI-generated geospatial data. This series now covers 560,000 km2, built using high resolution imagery and the Canada Centre for Mapping and Earth Observation’s geospatial foundation models and GeoAI Factory.
On this page
Learn about the GeoAI Series, the core geospatial data created using Artificial Intelligence (AI) developed by the Canada Center for Mapping and Earth Observation.
About GeoAI: fast, accurate and high resolution
The GeoAI Series is a collection of topographic features that are:
- Natural or artificial, including roads, buildings, lakes, rivers, and forested areas
- Based on high-resolution image sources
- Time enabled
- Automatically extracted by artificial intelligence algorithms
Why GeoAI?
GeoAI automates the data creation process, enabling Natural Resources Canada to drastically increase efficiency and boost its production rate. This allows us to quickly generate data and produce more up to date information when and where it’s needed.
Resolution
GeoAI features are generated from very high-resolution imagery (about 50 cm or better) thereby providing detailed and accurate data to users.
GeoAI accuracy
All AI models developed by NRCan are benchmarked against a comprehensive validation dataset, representative of the Canadian landmass. Only the highest performance models are retained to produce the GeoAI: GeoBase Series.
GeoAI for time-enabled analysis
The GeoAI data captures a topographic snapshot of an area at a specific moment in time. By layering multiple snapshots, users can easily view and analyze changes over time.
This example highlights an analysis of urban development for Quebec City between 2006 and 2022 created using GeoAI datasets.
GeoAI map and dataset gallery
GeoAI Data Index
Find areas that currently have GeoAI coverage and download the data.
GeoAI Feature Series
Access the GeoAI metadata and download the available datasets.
Urban Development Use Case
Explore the results of a change detection analysis in Québec City.
What's next for GeoAI
We’ve invested a lot of time and effort into building the groundwork for the GeoAI: GeoBase Series, but that’s just the first step. We still have plenty of room to grow, improve, and develop new features to transform this project into something truly cutting edge!
Things to look forward to shortly:
- Improved classification accuracy by developing and training new models with larger and more diverse training datasets.
- Improved horizontal accuracy, by matching the source images used to the very accurate High Resolution Digital Elevation Model (where available).
- Improved post-processing for cleaner and more representative features.
Check back regularly to keep informed on the latest updates and announcements about GeoAI.
Product background
Since 2019, the Canada Centre for Mapping and Earth Observation (CCMEO) has invested significant strategic efforts in developing cloud-based geospatial data management systems and advanced AI.
These efforts led to the launch and open distribution of the Geo-Deep-Learning project (access the geo-deep-learning github), aimed at enabling the use of Convolution Neural Networks (CNN) with georeferenced datasets.
Natural Resources Canada later implemented image pre-processing and geo-deep-learning tools in an operational pipeline which is used to automatically process very large quantities of aerial or satellite optical imagery (the use of other data sources is currently in development) to extract core geospatial features.
CCMEO is pioneering the integration of advanced foundation models specifically trained on Canadian multi-spectral multi-resolution and multi-temporal satellite and airborne imagery. By leveraging self-supervised learning techniques and vast archives of high-resolution Canadian earth observation data, these models are uniquely attuned to Canada’s diverse landscapes and environments. They can then be fine-tuned to automatically identify and analyze features of interest, enabling new levels of insight and innovation in geospatial analysis.
To test out the systems, validate the results, develop the dataset structure, and highlight use cases, various pilot projects were carried out in collaboration with the Canadian Council on Geomatics (CCOG). Following the completion of these pilot projects, the series was formally approved by the CCOG as a GeoBase Initiative data series.
Contact
Email geoinfo@nrcan-rncan.gc.ca to get in touch with our team, or contribute to the geo-deep-learning or geo-inference projects.
