In this webinar, the first-place winners of the Radiant Earth Spot the Crop data challenge on Zindi present their winning solutions to prediction crop types in Western Cape, South Africa. Raphael Kiminya from Kenya won the track that used satellite image time-series of Sentinel-2 multi-spectral data as input to his model. MG Ferreira from South Africa and Tien-Dung LE from Belgium teamed up to win the track that used Sentinel 2 and Sentinel-1 (radar) satellite data as input to their model. Mike Wallace from the Western Cape Department of Agriculture and Phillip Olbrich from GIZ will provide brief remarks, followed by a Q&A session. Watch the recording.
The call for nominations is open to any individual or team building agricultural-related applications for Africa. The Radiant MLHub Impact Award is a new annual prize organized by Radiant Earth Foundation. The award will recognize an individual or team contributing to real-world applications that address agriculture issues with geospatial training data found on Radiant MLHub. Launched in 2019, Radiant MLHub is an open library dedicated to Earth observation training data and machine learning models.
The Radiant MLHub Impact Award aims to raise awareness about geospatial data to tackle the urgent challenges of climate change, promote local solutions to local problems, and facilitate greater collaboration across organizations and individual practitioners.
The models include metadata based on the STAC ML Model Extension to enable easy sharing and retrieval. Radiant MLHub has been the source for high-quality open geospatial training data for use with machine learning (ML) algorithms since 2019. Today, we’re excited to announce the addition of a model repository allowing Radiant MLHub users access to both geospatial training data and ML models. The geospatial models catalog includes metadata that describes training data associated with a model and its architecture for training a model to generate predictions.
We have the pleasure of introducing Radiant Earth Foundation’s first online course, Machine Learning for Earth Observations (ML4EO) Bootcamp. Available on Atingi, an open digital learning platform designed to improve training and employment opportunities, this self-paced course contains a mixture of lectures and hands-on exercises for novice data science or remote sensing practitioners. Atingi is implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ).
Radiant Earth Foundation and RCMRD to Develop Joint Capacity Building Program focused on Machine Learning for Earth Observation in Africa
Radiant Earth and the Regional Centre for Mapping of Resources for Development (RCMRD) partnership aims to strengthen local geospatial and machine learning expertise for addressing global challenges in Africa.
Radiant Earth Foundation today announced a new partnership with the Regional Centre for Mapping of Resources for Development (RCMRD). This partnership will establish capacity building opportunities in geospatial analysis and machine learning (ML) for entities within RCMRD’s twenty Member States in Eastern and Southern Africa.
The Locus Charter promotes responsible practice in the use of location data across all sectors.
We are pleased to announce that we’ve joined the Locus Charter, together with the American Geographical Society (AGS), Association for Geographical Information (AGI), Environmental Information Systems Africa (EIS — Africa), the National Institute of Statistics and Geography (INEGI — Mexico), Open Geospatial Consortium (OGC), PLACE, Royal Geographical Society (RGS) and the Royal Institution of Chartered Surveyors (RICS).
The Locus Charter is a collection of ten international principles to support the ethical and responsible practice of individuals and organizations when using location data.
Designed to strengthen practitioners’ local capacity and skills in support of creating impactful machine learning applications.
We have the pleasure of introducing the online Machine Learning for Earth Observations (ML4EO) course. ML4EO technologies present a game-changing opportunity to identify and address unique, complex, and emerging challenges at local, regional, and global scales more accurately and more quickly. The plethora of EO data, combined with ML techniques, can help humanity see, understand, and respond more effectively to a rapidly changing world. But data scientists specializing in ML for EO are underrepresented, especially in Africa. Therefore, in May, we partnered with the Ugandan AI and data science research group at Makerere University to train a community of African regional experts collaborating on solutions to the continent’s needs and problems using ML4EO technologies.
Cloud Native Geospatial Ecosystem Community Releases STAC Specification version 1.0.0 to Connect Remote Sensing Data into a Network of Information about the Earth
The SpatioTemporal Asset Catalog (STAC) specification provides a common language to describe a range of geospatial information, so it can more easily be indexed and discovered.
WASHINGTON, June 10, 2021 (GLOBE NEWSWIRE) — The Cloud Native Geospatial Ecosystem Community announces the release of the SpatioTemporal Asset Catalog (STAC) specification version 1.0.0. The STAC specification is an open metadata standard that systemically describes remotely sensed data of the Earth. The specification supports emerging cloud-based geoprocessing engines by allowing spatial data to be indexed and discovered more efficiently. This feature is fundamental when building artificial intelligence applications using Earth imagery.
In December 2019, we publicly launched Radiant MLHub, the first open-access cloud-based repository for geospatial training datasets. Since then, we have continuously published new datasets and expanded the ecosystem around Radiant MLHub.
The idea of Radiant MLHub was born in Spring 2018 after several discussions and feedback from members of the community and funders. We had started a new project to develop a global and geographically diverse land cover training dataset using human verification called LandCoverNet. Soon after the launch of LandCoverNet in 2018, we identified a gap in the ecosystem to facilitate publication and uptake of training datasets in our community. That gap in the data value chain led us to the design and implementation of Radiant MLHub.