EODIE 1.1.0 Documentation

Purpose

EODIE is a toolkit to extract object based timeseries information from Earth Observation data.

The EODIE code can be found on Gitlab .

The goal of EODIE is to ease the extraction of time series information at object level. Today, vast amounts of Earth Observation data are available to the users via for example earth explorer or scihub. Often, not the whole images are needed for exploitation, but only the timeseries of a certain feature on object level. Objects may be polygons depicting agricultural field parcels, forest plots, or areas of a certain land cover type.

EODIE takes the objects in as polygons in a vector file as well as the timeframe of interest and the features (eg vegetation indices) to be extracted. The output is a per polygon timeseries of the selected features over the timeframe of interest.

Is EODIE suitable for me?

To use EODIE a general understanding of geospatial concepts is helpful. You will need:

Access to remote sensing data over your timeframe and area of interest (e.g. Sentinel-2/Landsat)
A geospatial vector file with polygons of your objects of interests - supported formats are shapefile, GeoPackage, GeoJSON, csv and FlatGeoBuf

EODIE is particularly designed for people wanting to exploit timeseries information of raster remote sensing data without the need for dealing with particularities of the data itself. EODIE produces human and machine readable csv files containing all information needed to start working with the data.

Gallery

Take a look at examples of what EODIE (and its auxiliary scripts) have been used for at Gallery

Installation

Please visit Installation for installation instructions using conda.

Getting started

You can test the usage of EODIE as command line tool by following the Small example .

Maintainers

Samantha Wittke, Finnish Geospatial Research Institute in the National Land Survey of Finland ([ORCiD](https://orcid.org/0000-0002-9625-7235))

Contributors

Eetu Puttonen ([ORCiD](https://orcid.org/0000-0003-0985-4443))
Juuso Varho
Petteri Lehti
Paula Litkey
Miloš Pandžić ([ORCiD]( https://orcid.org/0000-0003-4982-2630))
Mika Karjalainen
Arttu Kivimäki

Citation

EODIE- Earth Observation Data Information Extractor (2022) S. Wittke [online] , DOI: https://doi.org/10.5281/zenodo.4762323

Projects

Projects, enabled by EODIE or where EODIE or a derivative of EODIE has been used:

Project related to deforestation monitoring with Sentinel-1 funded by the European Space Agency (S14Science- Amazonas (http://project.gisat.cz/s14scienceAmazonas/ )),
Two projects related to crop and crop yield monitoring funded by Eurostat (CROPYIELD (https://www.luke.fi/projektit/cropyield/) and BIGDATA&EO (https://www.luke.fi/projektit/bigdataeo/),
A Business Finland co-creation project, working with Finnish companies in EO-business to find business opportunities (EODIE: 5332/31/2018),
Three projects related to forest phenology and crop monitoring funded by the Academy of Finland (AICropPro (publication https://doi.org/10.1371/journal.pone.0251952), decision numbers 315896 and 316172 (https://www.luke.fi/projektit/ai-croppro/) , BigData, grant-number: 295047, E. Puttonen fellowship, grant-number: 316096),
Multiple larger national agriculture related projects (such as DIGITALIS (https://www.luke.fi/projektit/digitalis-01/), Peltopiste (https://www.luke.fi/projektit/peltopiste/), Ikivihreä (https://www.luke.fi/projektit/ikivihrea-2/).

Contribution guide

Contributions can be made following the Contribution Guide

Acknowledgements

This project was initiated under the Academy of Finland research project 295047 in collaboration with Paula Litkey and Miloš Pandžić and has been supported also from the project 316096/320075. Part of the work has also been done under the umbrella of Academy of Finland flagship project UNITE (337656). Ms. Wittke acknowledges the PhD grant from Aalto School of Engineering. We are also grateful for the constructive comments on the code and documentation by the Nordic-RSE community (Richard Darst, Radovan Bast, Luca Ferranti, Enrico Glerean and Matthew West). The development of the EODIE Galaxy Tool has been supported by EOSC-Nordic, a project funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857652 and implemented by Anne Fouilloux.

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