Predicting plant species composition and its change in space and time at a fine resolution is useful for many scenarios related to biodiversity management and conservation, 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.

![graphical abstract](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1518097%2F4243b1e3ef9713ebcb27eb0a9614993d%2FGeoLifeCLEF2024.png?generation=1701864222703739&alt=media)