GeoEngine: A Platform for Production-Ready Geospatial Research

Geospatial machine learning has seen tremendous aca-demic advancement, but its practical application has been constrained by difficulties with operationalizing performant and reliable solutions. Sourcing satellite imagery in real-world settings, handling terabytes of training data, and managing machine learning artifacts are a few of the chal-lenges that have severely limited downstream innovation. In this paper we introduce the GeoEngine <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://apps.granular.ai/apps platform for re-producible and production-ready geospatial machine learning research. GeoEngine removes key technical hurdles to adopting computer vision and deep learning-based geospa-tial solutions at scale. It is the first end-to-end geospatial machine learning platform, simplifying access to insights locked behind petabytes of imagery. Backed by a rigor-ous research methodology, this geospatial framework em-powers researchers with powerful abstractions for image sourcing, dataset development, model development, large scale training, and model deployment. In this paper we pro-vide the GeoEngine architecture explaining our design rationale in detail. We provide several real-world use cases of image sourcing, dataset development, and model building that have helped different organisations build and deploy geospatial solutions.