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?
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.
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.
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…
A guide to identifying croplands with reasonable accuracy using a semantic segmentation model. We developed UNet-Agri, a benchmark machine learning model that classifies croplands using open-access Sentinel-2 imagery at 10m spatial resolution with ground reference data provided by the Western Cape Department of Agriculture in South Africa. This post is a step-by-step walkthrough of how we developed the model and evaluated its performance. Understanding what UNet-Agri does will help you build your model and deploy it for a similar application.
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).
Describing ML Models with the Geospatial Machine Learning Model Catalog (GMLMC).
During the height of the COVID-19 pandemic, the government of Togo launched a program to “boost national food production in response to the COVID-19 crisis by distributing aid to farmers”1. To accomplish this, the government needed accurate information about the distribution of smallholder farmers throughout the country. This kind of cropland map did not exist for the country, so they worked with NASA Harvest to rapidly develop a cropland map using AI. Finding enough high-resolution labeled training data to train the machine learning model was also a significant challenge, so the team combined global and local crowdsourced labels collected using the Geo-Wiki platform2 with hand-labeled imagery in targeted areas to train a new model for predicting crop areas.
Now featuring 250+ organizations that focus on machine learning applications with satellite data
The latest interactive Machine Learning for Earth Observation Market Map, a curated list of organizations focused on different machine learning aspects with a satellite data pipeline, is available for download. This release includes an additional list of 100+ organizations, thanks to a crowdsourcing effort on social media. Earlier in September, we asked our followers on Twitter and LinkedIn to identify organizations that we missed in the earlier version of the market map or were established since then. The large number of contributions from people in such a short period speaks of the niche area of machine learning (ML) for Earth observation (EO). The entries hint toward the incredible aptitude of organizations to optimize these innovative technologies and expand them in the service of humanity.