Use Cases

ii2030 Challenge: AI for Earth Observation. Who are the users?

Discover data usage scenarios for open machine learning ready Earth observation repositories like Radiant MLHub. In about one month, we will launch the inclusive innovation 2030 (ii2030) process, where key stakeholders will assemble to turn a systemic challenge into an opportunity for all of us. We will attempt to answer a critical question: “How might we ensure open access to high-quality machine learning (ML) ready Earth observation (EO) data on a sustainable basis?” The ii2030 challenge will support open repositories like Radiant MLHub, which hostsa collection of geospatial training datasets and ML models.

Community Voices

Radiant MLHub Spotlight Q&A: Emmanuel Siaw-Darko

Our Community Voice for this quarter is Emmanuel Siaw-Darko. He joined Radiant Earth as a Machine Learning Intern after winning third place in the AI4FoodSecurity data challenge for his model to classify crop types in South Africa and Germany. In this Q&A, Emmanuel talks to us about his data science journey and working at Radiant to build baseline models that data scientists can use to compare their algorithms.


Announcing Executive Leadership Transition

Washington, July 5 — Radiant Earth Foundation today announced a plan for an executive leadership transition later this year. Effective November 1, 2022, Hamed Alemohammad, Executive Director and Chief Data Scientist, will transition to an advisor and member of the Board of Directors at Radiant Earth Foundation. With this transition, the Board of Directors has opened a position to hire the new Executive Director for the organization.

Machine Learning

Producing Global Training Dataset Labels

Radiant Earth Foundation, a nonprofit empowering organizations with open geospatial training data, models, and metadata standards, has been supporting the ramp project’s labeling efforts for building rooftops. Producing building footprint data is a methodical task that requires polygons to be labeled over satellite and drone imagery, usually by hand on a computer, to provide context or confirm the details of each polygon. The labels paired with the imagery become the training data inputs for a building footprint extraction model. In this blog post, we discuss the labeling process, answering a fundamental question: How can we ensure generating high-quality labels working with remote teams?


Cloud-Native Geospatial Outreach 2022 Recap (and videos!)

In April over 70 speakers and 800 participants came together for the incredible Cloud-Native Geospatial Outreach Event. Our goal for the event was to highlight just how far the movement around COGSTACZarr & COPC has come, and to accelerate its path towards becoming the way to make geospatial information accessible to the world. Almost everyone who attended was blown away by the breadth and depth of what was covered, and it was clear that cloud-native geospatial is already having a substantive impact on the world.

Machine Learning, News

ii2030 Challenge: AI for Earth Observation

Identifying sustainable business models for open machine learning ready Earth observation repositories

When Gedeon Jean first realized the power of Earth observation (EO) data to detect environmental changes, he was mesmerized. As a Machine Learning Research Engineer, he saw the potential of combining machine learning (ML) and EO to develop diverse predictive applications for Rwanda, his native country. Rwanda is increasingly experiencing natural disasters due to climate change, including landslides, floods, and earthquakes, which take a socio-economic toll on an already vulnerable population.

Community Voices, Machine Learning

Data Challenge Winner: Q&A with Christian Ayala Lauroba

A conversation with the First Place winning team’s lead of the AI4FoodSecurity Data Challenge.

Hosted on ESA’s AI4EO platform, the AI4FoodSecurity data challenge brought together participants worldwide to find the best machine learning/AI solutions for crop identification using Planet Fusion data and Sentinel-1 and -2 data. The challenge covered two areas of interest, Germany and South Africa, with high-quality cadastral data on field boundaries and crop types as ground truth input.

Machine Learning

Enabling Agricultural Dataflows in Radiant MLHub for Geospatial Machine Learning Analytics

How Radiant MLHub strengthens the data collection to analytics pipeline for agriculture projects.

Radiant Earth Foundation is strengthening geospatial machine learning (ML) workflows for organizations working on agriculture projects by streamlining the process, from ground reference data collection to insight analytics, through Radiant MLHub.

Radiant MLHub is an open-access library dedicated to geospatial training data and ML models. Since its inception in 2019, Radiant has focused on developing and aggregating geo-diverse benchmark data that practitioners can use to create applications and enable data-driven policies that impact lives worldwide. The datasets vary from…

Community Voices

Radiant MLHub Spotlight Q&A: Victor Faraggi

Accelerating climate change applications with machine learning models and remote sensing data.


Next Week: See an Incredible Selection of Cloud-Native Geo Talks

I am very pleased to announce the schedule for the Cloud-Native Geospatial Outreach Event that I’m organizing with the Open Geospatial Consortium next week (April 19th and 20th). We’ll have over 70 5-minute lightning talks, along with 6 in-depth tutorials, from an incredible set of speakers. The event will be entirely virtual and completely free and is spread across time zones to be friendly to a global audience. The talks will also be posted as quickly as possible, so everyone can see the ones that aren’t in the right time zone.