During ESA Φ-week 2019, we introduced our AI @ Earth Observation Support tool: Hugin, a tool which is generally available on GitHub and our internal GitLab

In the last decade we witnessed a massive technological breakthrough in Computer Science, particularly the Cloud gained increased attention both inside and outside of the IT community. Also in the last few years there was a massive shift towards Machine Learning, with many IT companies investing significant resources in developing new techniques and products aimed at bringing Machine Learning closer to the developer communities, and finally to the end-user.

Unfortunately most of these tools and techniques are targeted at the Computer Vision and NLP communities and do not aim to tackle the issues faced by the Earth Observation community: geo-referenced data, hyperspectral data, coregistration, vectorial data, various eccentric data types, etc.

During our presentation we introduced Hugin, a tool aimed at bridging the gap between the latest Machine Learning tools and the Earth Observation ecosystem, facilitating the adoption of the latest ML tools for geospatial applications. Hugin brings together Machine Learning tools like Keras, TensorFlow, SciKit-Learn and tools specific to the Earth Observation community: GDAL, rasterio, Fiona. It aims to provide solutions for problems like coregistration, resampling, mask generation, ensembling and augmentation allowing the EO researcher to focus on their algorithms and models.

Hugin simplifies access to various types of machine learning infrastructures: it provides support for distributed training, support for cloud based ML services (Google Cloud ML), support for Kubeflow, etc. By building on top of GDAL and rasterio, Hugin supports the most common EO data (including native access for Sentinel-1 and Sentinel-2 data).

This work was initially motivated by the Urban 3D and SpaceNet RoadDetector challenges, particularly by the need to provide a reusable and extensible Machine Learning framework. The tool was further developed as part of the ESA ML4EO project, providing the core training and classification facilities required by project use cases. We also consider that Hugin represents a tool that can facilitate EO researchers experimentation with state of the art machine learning techniques.