Community Voices

Zhuangfang NaNa Yi: Building Machine Learning Applications that Empower Policymakers with Insights to Support Vulnerable Communities

A conversation about the nuances of applying machine learning algorithms to Earth observation for global development organizations.

It is our pleasure to Dr. Zhuangfang NaNa Yi, a machine learning engineer at Development Seed, supporting international development organizations like UNICEF, the World Bank, and USAID in making data-driven policy decisions. She has extensive experience in applying machine learning algorithms to geospatial and satellite data, from building applications that farmers can use to track crop types and changes to water bodies, mapping forest and measuring food security, and more.

Standards

The Tools to Make Spatial Data More Open

How the STAC tool ecosystem is growing towards supporting truly open spatial data.

The STAC specification is transforming the way data providers and consumers are thinking about how to work with open spatial data. It provides a clear, common language for spatial data that is flexible and developed by an open community. But what is a language without the tools that speak it? The open-source STAC tooling ecosystem is a critical part of making spatial data open and accessible for use. In this post, I’ll talk about why access to that tooling provides is so important, and describe two instances of collaboration I experienced in the recent STAC and Cloud Native Geospatial sprints that, to me, exemplifies the growth of STAC tooling.

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.

Machine Learning, News

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.

Machine Learning, News

Data Labeling Contest – Cloud Native Geospatial Sprint

Standards

Welcoming New Collaborators to the Cloud Native Geospatial Ecosystem

I wanted to share some background and more details about the Cloud Native Geospatial Outreach Day and Sprint (1 week away! September 8th — signup here), which has grown out of the SpatioTemporal Asset Catalog (STAC) community sprints. One of our goals for STAC has been to make it a truly collaborative community, and one that is welcoming as possible. We believe that the best standards are forged from diverse use cases and perspectives coming together and collaborating. We started with a gathering of 25 people from 14 organizations and have always sought to bring more people into the fold.

Machine Learning, News

Radiant Earth Foundation Awarded Cooperative Agreement from NASA