GeoLifeCLEF2022
Overview
The aim of this competition is to predict the localization of plant and animal species.
To do so, 1.6M geo-localized observations from France and the US of 17K species are provided (9K plant species and 8K animal species).
These observations are paired with aerial images and environmental features around them (as illustrated bellow).
The goal is, for each GPS position in the test set (for which we provide the associated aerial images and environmental features), to return a set of candidate species that should contain the true observed species.
Automatic prediction of the list of species most likely to be observed at a given location is useful for many scenarios related to biodiversity management and conservation.
First, this would allow to improve species identification tools - automatic, semi-automatic, or based on traditional field guides - by reducing the list of candidate species observable at a given site.
More generally, it could facilitate biodiversity inventories through the development of location-based recommendation services (e.g. on mobile phones), encourage the involvement of citizen scientist observers, and accelerate the annotation and validation of species observations to produce large, high-quality data sets.
Finally, this could be used for educational purposes through biodiversity discovery applications with features such as contextualized educational pathways.
Competition
Start Date: 9 March 2022
End Date: 24th May 2022
Kaggle URL: https://www.kaggle.com/c/geolifeclef-2022-lifeclef-2022-fgvc9/
Github URL: https://github.com/maximiliense/GLC
Organizers
Titouan Lorieul
Elijah Cole
Benjamin Deneu
Alexis Joly
Maximilien Servajean
Acknowledgements
This project has received funding from the French National Research Agency under the Investments for the Future Program, referred to as ANR-16-CONV-0004, and from the European Union's Horizon 2020 research and innovation program under grant agreement No 863463 (Cos4Cloud project).
