Machine Learning

Advancing AI for Earth Science: A Data Systems Perspective

Tackling data challenges and incorporating physics into machine learning models will help unlock the potential of artificial intelligence to answer Earth science questions.

The Earth sciences present uniquely challenging problems, from detecting and predicting changes in Earth’s ecosystems in response to climate change to understanding interactions among the ocean, atmosphere, and land in the climate system. Helping address these problems, however, is a wealth of data sets—containing atmospheric, environmental, oceanographic, and other information—that are mostly open and publicly available. This fortuitous combination of pressing challenges and plentiful data is leading to the increased use of data-driven approaches, including machine learning (ML) models, to solve Earth science problems.

Machine Learning, News

Machine Learning for Earth Observation Market Map

Meet the 100+ organizations that focus on machine learning applications with satellite data

Building geospatial machine learning applications involve many dependable moving parts, from accessing Earth observation (EO) data, labeling imagery, and generating training data to creating and developing models and running analytics. A growing list of organizations from various sectors are providing solutions and services to advance these applications. Who can help you build machine learning applications, identify patterns from your data, or run your crowdsourcing campaign? What organizations are providing software or a platform that you can utilize to develop your machine learning model?

Machine Learning

Using Generative Adversarial Networks to Address Scarcity of Geospatial Training Data

Results show models based on Generative Adversarial Networks perform better than Convolutional Neural Networks in classifying land cover classes outside of the training dataset.

In many supervised machine learning (ML) applications that use Earth observations (EO), we rely on ground reference data to generate training and validation data. These reference data are the building block of those applications and require geographical diversity if one aims to deploy the models across various geographies. Ground reference data collection, however, is an extensive process and extremely scarce in remote areas that would most benefit from the use of EO.

Machine Learning, Standards

Cloud Native Geospatial Outreach Day Recap

Chris Holmes, Technology Fellow at Radiant Earth gives a recap of the Cloud Native Geospatial Outreach Day and shares some of his favorite parts.

It’s been just over three weeks since the Cloud Native Geospatial Outreach Day. Everyone I’ve talked to felt it was an incredible event, and I definitely concur. Thankfully we managed to record almost all of it, so if you missed it you can still catch the content on youtube!

We opened with a welcome from Bruno Sánchez-Andrade Nuño and me, representing the Microsoft and Planet, the convening sponsors. Then Hamed, the new Executive Director of Radiant Earth, introduced the Data Labeling Contest (which was a great success).

Machine Learning, News

Cloud-Spotting at a Million Pixels an Hour

Jon Engelsman won the Best Quality Labeler award for our recent Data Labeling Contest. We asked him to detail his approach and workflow.

I recently attended the Cloud Native Geospatial Outreach Day, a virtual event designed to “introduce STACCOG, and other emerging cloud-native geospatial formats and tools to new audiences.” As part of the outreach day, co-sponsors PlanetMicrosoftAzavea, and Radiant Earth teamed up to host a week-long data labeling contest. This friendly competition had contestants race to manually label the shapes of clouds across a large selection of satellite images from around the world. The contest’s ultimate goal was to generate a crowd-sourced collection of high-quality labeled images, data that can be used to train accurate cloud detection models.

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Announcing the Winners of the Data Labeling Contest

Earlier this month, we organized a data labeling contest as part of the Cloud Native Geospatial Outreach Day sponsored by Planet, Microsoft, and Azavea. The contest was designed as a crowdsourcing campaign to encourage the global community to contribute to open-access training data catalogs. Participants were asked to identify cloudy pixels in Sentinel-2 scenes.

The labeling contest was conducted on GroundWork, Azavea’s annotation tool designed for geospatial data. We were amazed by the high participation worldwide and the community’s excitement to help develop a large-scale accurate cloud detection training dataset. In the end, 231 users around the world signed up and labeled 75,645 tasks, which equates to about 2 million km2 of classified Sentinel-2 imagery.

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Data Labeling Contest – Cloud Native Geospatial Sprint

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Radiant Earth Foundation Awarded Cooperative Agreement from NASA

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Radiant Earth Foundation Releases the Benchmark Training Data “LandCoverNet” for Africa

LandCoverNet is an open-access global land cover classification training dataset with satellite image pixels labeled for seven land cover classes.

Radiant Earth Foundation is proud to announce the release of “LandCoverNet,” a human-labeled global land cover classification training dataset. Available for download on Radiant MLHub, the open geospatial library, LandCoverNet will enable accurate and regular land cover mapping allowing for timely insights into natural and anthropogenic impacts on the Earth. This release contains data across Africa, which accounts for ~1/5 of the global dataset.

Machine Learning, News

Radiant MLHub Hosts STAC-Compliant SpaceNet’s Datasets

Founded in 2016 to accelerate open source geospatial machine learning, SpaceNet is a nonprofit organization that runs data challenges and releases the training datasets, baseline algorithms, winning algorithms, and detailed evaluations under an open source license. They have organized six data challenges to date, each focusing on a different problem that applies machine learning to satellite imagery to solve complex mapping problems.