GeoEngine: A Platform for Production-Ready Geospatial Research

Sagar Verma, Siddharth Gupta, Hal Shin, Akash Panigrahi, Shubham Goswami, Shweta Pardeshi, Natanael Exe, Ujwal Dutta, Tanka Raj Joshi, Nitin Bhojwani; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 21416-21424

Geospatial machine learning has seen tremendous academic 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 challenges that have severely limited downstream innovation. In this paper we introduce the GeoEngine platform for reproducible and production-ready geospatial machine learning research. GeoEngine removes key technical hurdles to adopting computer vision and deep learning-based geospatial 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 rigorous research methodology, this geospatial framework empowers researchers with powerful abstractions for image sourcing, dataset development, model development, large scale training, and model deployment. In this paper we provide 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.