Introducing eo-learn

Bridging the gap between Earth Observation and Machine Learning

Devis Peressutti
Jul 31, 2018 · 6 min read
Example of remote sensing workflow that can be build using eo-learn. This workflow is used to create a global service for water-level monitoring of reservoirs and water bodies.

In a nutshell

Example of spatial data that can be stored in an EOPatch in raster and vector format. These data are needed to build a machine learning model for LULC map classification. In addition, non-spatial data as well as any data format readable in Python can be stored in an EOPatch.
Example of NDVI trends derived from Sentinel-2 over a year of observations. Red shows values for cultivated land, blue for build-up area, and green for grassland. eo-learn provides tasks to handle spatio-temporal processing such as masking and filtering of cloudy observations (empty circles), and interpolation of valid data (filled circles) to generate an interpolated time-series (continuous line). Different interpolation methods (e.g. linear, univariate spline, B-spline, Akima) have been implemented.

Example applications

Example of water-level segmentation using multiple sources, in particular Sentinel-1 (left), Sentinel-2 (middle) and Digital Elevation Model (right). Using multiple sources leads to a more accurate delineation of the water body.
Time-lapse of Ouarzazate Solar power station in Morocco. Originally created by Simon Gascoin on Twitter.
Time-lapse after frame co-registration using a rigid transformation. Misalignments can be seen for initial frames as registration errors accumulate over the time-series.

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Thanks to Matej Aleksandrov, Anze Zupanc, Grega Milcinski, Matej Batič, and Katja Bajec.

Devis Peressutti

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Data scientist passionate about earth and medical images.

Sentinel Hub Blog

Stories from the next generation satellite imagery platform