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.

Submissions to the new Datasets and Benchmarks track require data documentation and availability on an open repository.

Organizers of the NeurIPS 2021 conference recently announced a new track for Datasets and Benchmarks. This is a significant development for a major machine learning (ML) conference to highlight the importance of data in developing algorithms for real-world problems. We at Radiant Earth Foundation welcome this initiative and applaud the organizers for establishing this new track.

In recent years, there have been many discussions and arguments to incentive ML researchers to work on real-world problems. One of those incentive mechanisms is the opportunity to publish a paper in a peer-reviewed conference, and getting recognition for working on these problems. The new track at NeurIPS is a necessary step to realize these incentives.

Generating a global training dataset while supporting social initiatives and sustainable practices.

Labeling satellite imagery is the process of applying tags to scenes to provide context or confirm information. These labeled training datasets form the basis for machine learning (ML) algorithms. The labeling undertaking (in many cases) requires humans to meticulously and manually assign captions to the data, allowing the model to learn patterns and estimate them for other observations.

For a wide range of Earth observation applications, training data labels can be generated by annotating satellite imagery. Images can be classified to identify the entire image as a class (e.g., water body) or for specific objects within the satellite image. However, annotation tasks can only identify features observable in the imagery. For example, with Sentinel-2 imagery at the 10-meter spatial resolution, one cannot detect the more detailed features of interest, such as crop types but would be able to distinguish large croplands from other land cover classes.

Using the Python client to discover and download training datasets without managing API requests.

We are excited to announce the first beta release of the radiant_mlhub library, a Python client for working with the Radiant MLHub API! With this release, users can work with Radiant MLHub datasets through an intuitive Python interface without having to worry about constructing API requests and managing authentication.

The library is still in the early stages of development, but we encourage you to try it out and give us feedback on how well it addresses your use-cases. This article will walk you through the process of installing and configuring the library, navigating datasets and their collections, and downloading training datasets. For more detailed documentation of the Python library, please see the official documentation here. A basic knowledge of Python programming is recommended.