Grants and Contributions

Titre: ICEBERG

Put in place a Web platform for client orders to optimize imagery processing and classification.

Title: Deep Learning for Classification of SAR-Derived Forest Change

Use Canadian radar technology, combined with AI algorithms, to detect and map forest changes.

Title: Mixed Relevant Features Analysis for Deep Learning Ship Detection in RADARSAT Constellation Mission Data

Incorporate RADARSAT Constellation Mission data into an existing AI-enabled Amari platform for automated ship detection.

During the Apollo program, lunar surface
operations were hampered by the effects of
fine, pervasive, highly adhesive lunar dust.
The missions have shown challenges like
obscuration of vision, clogging of
equipment, dust covering/abrasion of many
sensitive external surfaces, degradation of
seal and thermal performance. It became
clear that before extensive lunar
exploration efforts can continue, strategies
must be developed to mitigate the effects of
lunar dust.

The project aims at providing a solution to
this challenge by advancing the development
and enhancement of Integrity Testing
Laboratory's (ITL) unique cornerstone lunar
Dust Mitigation Technology for sensitive
materials of external space structures that
will be used in robotic and manned missions
for Moon exploration. One of the major tasks
in this project will include participating
in a coming Lunar mission that will allow
testing ITL samples and prototype
spectrometers in real Lunar environment
conditions. The data from this Lunar dust
exposure experiment will be evaluated and
compared to data from ground-based exposures
of similar samples in ITL's upgraded lunar
environment simulator. Another major task in
the project will see building two prototypes
of portable spectrometers for evaluation of
major thermal optical properties for use in
planetary space environments. The results
from this project will serve as a foundation
for creating greater capacity to simulate
the Moon's dusty environment for further
testing and validation on Earth of Integrity
Testing Laboratory's technologies.

Government authorities and data scientists do not have the ability to directly request ground truth observations from citizens to assess the validity of space-based Earth observation (SBEO) algorithms, and citizens do not have the ability to submit such observations directly. The opportunity is to combine these two observational approaches in a completely new method of environmental and disaster assessment using SBEO techniques and imagery. This will help close a communication gap and allow scientists and government officials to collaborate with citizens to validate and enhance the satellite image processing algorithms directly and in near real time, to determine the ground truth.

The objective of this project is to create an automated data processing pipeline and provide access to this data through a web visualisation platform that can be used for monitoring infrastructure extending across thousands of kilometers, while still highlighting displacement areas as small as tens of meters. 3vG will develop more efficient techniques to capture displacement features at all scales and displacement feature highlighting and filtering to allow end users to quickly focus in on areas of critical importance. The end result will be higher quality, lighter weight, and easier to use data products for network wide infrastructure monitoring.

This project develops a commercial capacity to monitor freshwater lake ice hazards from space using satellite radar and distribution of ice hazard maps to user’s cell phones and desktop computers in near real-time. NextGen Environmental Research will also build a capacity for rapid classification of radar data into ice types and hazards using Deep Learning algorithms. The lake ice hazard monitoring service will process large volumes of radar image data to help make recreational and remote communities that rely on lake ice to travel more safely and be more climate resilient in the future.

SkyWatch Space Applications will develop a state of the art cloud-enabled cataloging, ordering, searching, processing, distribution and collaboration platform for Earth Observation (EO) data, catering to the unique needs of large organizations and governments. This will enable efficiencies by centralizing ordering, orchestrating data distribution, and secure collaboration to obtain RADARSAT Constellation Mission (RCM) and other commercial data sources. SkyWatch Space Applications will collaborate with expert remote sensing communities and large organizations who make significant use of EO data, to develop a solution which will better serve all users.

CANGrow is a novel modular polyculture indoor food production system that will enable long term space exploration. The CANGrow system incorporates novel technologies in order to offer the widest possible variety of foods with optimal yield. It grows a diversity of biologically efficient food products: strawberries, cherry tomatoes, two root vegetables, microgreens, four unique culinary herbs, mini-head lettuce, an algae superfood, and a mycelial meat substitute - “space bacon”. The technology has the potential to provide over 500kg of nutrient dense food annually.

CANGrow is a novel modular polyculture indoor food production system that will enable long term space exploration. The CANGrow system incorporates novel technologies in order to offer the widest possible variety of foods with optimal yield. It grows a diversity of biologically efficient food products: strawberries, cherry tomatoes, two root vegetables, microgreens, four unique culinary herbs, mini-head lettuce, an algae superfood, and a mycelial meat substitute - “space bacon”. The technology has the potential to provide over 500kg of nutrient dense food annually.