Start Date: February 29 End Date:  24 May Competition URL: Kaggle

Motivation

Predicting plant species composition and its change in space and time at a fine resolution is useful for many biodiversity management and conservation scenarios, improving species identification and inventory tools, and educational purposes.

This challenge aims to predict plant species in a given location and time using various possible predictors: satellite images and time series, climatic time series, and other rasterized environmental data: land cover, human footprint, bioclimatic, and soil variables.

To do so, we provide a large-scale training set of about 5M plant occurrences in Europe (single-label, presence-only data) as well as a validation set of about 5K plots and a test set with 20K plots, with all the present species (multi-label, presence-absence data).

The difficulties of the challenge include multi-label learning from single positive labels, strong class imbalance, multi-modal learning, and large-scale.

Context

This competition is held jointly as part of:

The participants are required, in order to participate in the LifeCLEF lab to register using this form (and checking "Task 3 - GeoLifeCLEF" of LifeCLEF). 

Only registered participants can submit a working-note paper to peer-reviewed LifeCLEF proceedings (CEUR-WS) after the competition ends.

This paper should provide sufficient information to reproduce the final submitted runs. Only participants who submitted a working-note paper will be part of the officially published ranking used for scientific communication.

Timeline

Unless otherwise noted, all deadlines are at 11:59 PM CET on a corresponding day. The competition organizers reserve the right to update the contest timeline if they deem it necessary.

Organizers

Credit


This project has received funding from the European Union’s Horizon research and innovation program under grant agreement No 101060639 (MAMBO project) and No 101060693 (GUARDEN project).