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.


Sentinel Hub Blog

Stories from the next generation satellite imagery platform

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

Devis Peressutti

Written by

Data scientist passionate about earth and medical images.

Sentinel Hub Blog

Stories from the next generation satellite imagery platform