Grants and Contributions

Title: Automated plume detection algorithm for high-resolution methane measurements from GHGSat satellites

Develop a new AI algorithm to detect methane emissions plumes measured by GHGSat's satellites, with little or no human intervention.

Title: Deep learning system for enhanced spatio-temporal maritime analytics using big data streams from new Canadian EO constellations

Develop a flexible and powerful AI capability that will benefit shipping lines, port and terminal operators and maritime regulatory authorities.

Title: Cloud-Based Delivery of Space-Based Analytics

Build a cloud-based computing architecture that reliably predicts the costs for space data storage, delivery and use of cloud-based data analytics for projects of varying sizes.

Title: Evaluating Earth Observation Data and Deep Learning Methods to Support Landscape Disturbance Mapping

Develop an EO-based solution through the use of deep learning methods to identify landscape disturbance areas for habitat restoration purposes for study sites in northeastern BC and northwestern Ontario.

Title: Development of Multi-Source Analysis Ready Data based on the fusion of Sentinel-1, Sentinel-2 and NorthStar-like hyperspectral data

Use advances in multi-source date fusion and machine learning techniques to characterize a wetland area.

Title: Combining deep learning techniques with cloud computing and big remote sensing data towards a better situation understanding

Develop a new cloud-based solution to process a high volume of remote sensing images using deep learning techniques for different applications, such as flood monitoring, ship detection and land use/cover classification.

Title: Improving Space-Based Radar Reflectometry for Better Ocean State and Target Monitoring Using Advanced Data Processing

Improve the performance of space-based radar reflectometry for ocean-surface target monitoring by using state-of-the-art data processing technology.

Situational awareness is a key capability required by most modern exploration missions today. Government and commercially-leveraged missions alike increasingly need more compact, affordable imaging systems capable of delivering a range of data products. At the same time, mission constraints drive a need for improved onboard processing, autonomy and artificial intelligence to minimize volume of Earth-Moon data transfer, all while keeping costs low.

This project aims to address these needs and challenges via demonstration and performance characterization of a Smart Lunar Vision System and its different functionalities ahead of future applications in human and robotic exploration. The system developed by Canadensys features a low-mass, low power 360° imaging system, with applications spanning Low Earth Orbit (LEO), Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) out to the Moon, Mars and beyond. The LEAP Capability Demonstration project builds flight heritage for the system in cislunar space, ensuring a system that is both operationally ready and commercially competitive.

Space agencies, research institutes and private organizations are looking for commercial landing services in order to reach sites of interest on the Moon. The landing systems will need to be equipped with high-accuracy navigation systems to accurately reach their target. Since there is no GPS constellation around the Moon for real-time localisation, or other readily available commercial systems to answer this need, a novel approach is needed to provide positioning of orbiters or landers around the Moon.

The “Crater-Based Absolute Navigation Demonstration” project aims at filling this need by demonstrating a GPS-like system in support for future Moon exploration missions. This system will use features on the ground – Moon craters – as references to autonomously determine the position of orbiters and landers in Moon geographical coordinates. This will increase the accuracy at touchdown by orders of magnitude with respect to current capabilities and help meet the challenging requirements of future Moon precision landing missions.

This proposal exploits recent technological advancements to deploy an array of world-leading fluxgate magnetometers to provide freely available science measurements in support of satellite missions such as CSA's Cassiope/ePOP, NASA's THEMIS, ESA's SWARM and JAXA's ARASE.