ASL has collaborated with Defence Research and Development Canada (DRDC) for over a decade, supporting a range of research initiatives focused on the application of geospatial analytics and artificial intelligence to defence-related challenges.
ASL has signed a contribution agreement with the Canadian Space Agency for a 2-year project under the Satellite Mobilization for Biodiversity (SaMBA) opportunity, part of the smartEarth funding initiative.
ASL contributed to the Canadian Department of National Defence (DND) IDEaS program to address challenges in space-based intelligence, surveillance, and reconnaissance (SB-ISR), focusing on detecting concealed human activity in remote regions.
Malahat First Nation and Transport Canada, initiated a project to develop methods for mapping bull kelp in the southern Gulf Islands of British Columbia using high-resolution optical satellite imagery in support of assessing cumulative effects of marine shipping. The work was performed by ASL and project partners Sarah Schroeder and Dr. Maycira Costa of the University of Victoria.
The study compared two classification approaches—object-based and pixel-based methods—to detect floating kelp beds and map their distribution across the study area. Satellite imagery was acquired and processed to develop kelp detection workflows and evaluate the performance of each approach, producing spatial datasets to support future monitoring and marine management.
Project outputs included:
ASL’s remote sensing team aided in a pre-feasibility study for the Government of the Northwest Territories to support a potential natural gas export project situated in the Mackenzie Delta. To evaluate the viability of year-round shipping, the team provided the following key analyses:
ASL has signed a contribution agreement with the Canadian Space Agency for a 2-year project under the Satellite Mobilization for Biodiversity (SaMBA) opportunity, part of the smartEarth funding initiative.
ASL is in works to develop the DeCAF Rapid Response Tool (RRT) to support near-real-time monitoring of salmon habitats. It builds on ASL’s wide-area-monitoring solution DeCAF, and uses advanced deep learning techniques to process open-access or commercial satellite data and find immediate landscape changes, including human disturbances and natural phenomena (i.e. landslide detections are shown in adjacent figure).
ASL is developing this in collaboration with a variety of stakeholders:
In 2021, ASL completed a project for the Greenland Ministry of Mineral Resources (MMR) to process and analyze a large amount of airborne hyperspectral data over Gardar Province in south Greenland. The airborne data was collected over the Ilímaussaq intrusion and Igaliku Igneous Complex. The survey area is known to be rich in rare earths, but also contains underexplored segments of interest to mineral prospectors.
ASL generated mineral maps for high priority areas which in turn are to be used to support revision of geological knowledge and geoscience information for the mineral exploration industry.
Project services included:
ASL contributed to the Canadian Department of National Defence (DND) IDEaS program to address challenges in space-based intelligence, surveillance, and reconnaissance (SB-ISR), focusing on detecting concealed human activity in remote regions.
The project developed methods to identify subtle landscape changes—such as soil disturbances and infrastructure development—using satellite imagery. Emphasis was placed on wide-area monitoring beyond routinely observed locations, enabling the detection of emerging activity in sparsely monitored environments.
Using automated time-series analysis and change detection, ASL built workflows to process large satellite datasets, flag anomalies, and support targeted follow-up with higher-resolution sensors.
Project outputs included:
