Grants and Contributions

This 27-month project will support a feminist response and recovery from the current impacts of COVID-19, through systemic change. Achēv will do this by conducting a broad consultation with Greater Toronto Area stakeholders to gain perspectives on existing practices and barriers faced by immigrant and racialized women in employment, namely the lack of equality and inclusion in recruitment, advancement and retention. Achēv and its partners will develop key resources and launch a series of interventions, including information sessions, social media campaigns, town hall meetings, and tools and training workshops on implementation of inclusive practices for employers. The organization will develop resources on best practices that promote economic security and prosperity for immigrant and racialized women. At the completion of the project, a final report will be produced that includes the results achieved, lessons learned, recommendations and conclusions.

At the end of the project, the organization will have contributed to addressing systemic barriers by advancing inclusive policies and practices, supporting positive distribution of authority, voices, and decision-making power, and increasing networks and collaboration to accelerate systemic change to support women’s equality.

To acquire install and deploy technologies for managing traffic and transportation To construct and/or rehabilitate transportation infrastructure to support the movement of goods and people

80th Anniversary WWII Diorama Exhibition & Presentation

Expand production and modernize operations for honey processing in Manitoba - Budget 2021

Restoration and protection of habitats of importance to the Least Bittern through the control of invasive plants in Lake Saint-Pierre

Development of Indigenous Capacity Through Ongoing Caribou Recovery Actions; Restoration of Habitat; and Foodscape of the Klinse-Za Caribou Herd

This project provides an unclassified multi-domain situational awareness tool to merge the airborne track picture (Automatic Dependent Surveillance–Broadcast) with the maritime track picture (Automatic Identification System) integrated with geospatial decision support tools providing rapid situational awareness accessible anywhere in the world via space-based Internet access. This near real-time picture of vehicle movements, combined with real-time weather and geospatial data will allow commanders to make informed decisions, anywhere, anytime wherever deployed around the world.

KorrAI Technologies is combining Interferometric Synthetic Aperture Radar (InSAR) with cloud computing and artificial intelligence to provide an efficient framework to automatically detect changes in surface movement. With the RADARSAT Constellation Mission (RCM) repeat interval of 4 days in Canada, KorrAI Technologies’s solution will provide near real-time, dynamic surface deformation monitoring. KorrAI Technologies proposes to commercialize the petabytes of over twenty years’ worth of historical radar data collected from the previous RADARSAT-1 mission, as well as ongoing data collected by the RCM mission, providing Canadian firms with an unprecedented level of data access and analytical capabilities.

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.

This project builds on the Arctic Eider Society's online platform and mobile app called SIKU: The Indigenous Knowledge Social Network, to deliver an enhanced SIKU Ice Map. The project will incorporate RADARSAT Constellation Mission data and other satellite imagery, observations made by Inuit community members, drone data, and machine learning techniques to identify dangerous conditions such as polynyas within the landfast ice. It will create the first Indigenous-trained algorithm delivering EO-derived products for the benefit of safe sea ice travel and climate change research, including the classification of EO observations into both Inuktitut and Western scientific sea ice terminologies.