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GeoLifeCLEF 2020

Location-Based Species Recommendation

Motivation

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, it could improve species identification tools (whether automatic, semi-automatic or based on traditional field guides) by reducing the list of candidate species observable at a given site. More generally, this 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. Last but not least, this could be used for educational purposes through biodiversity discovery applications with features such as contextualized educational pathways.

Data collection

The challenge will rely on a collection of millions of occurrences of plants and animals in the US and France (primarily from GBIF , iNaturalist , Pl@ntNet and a few expert collections). In addition to geo-coordinates and species name, each occurrence will be matched with a set of geographic images characterizing the local landscape and environment around the occurrence. In more detail, this will include: (i) high resolution (about 1 meter per pixel) remotely sensed imagery (from NAIP for the US and from IGN for France, (ii) bio-climatic rasters from WorldClim (1 km resolution) and (iii), land cover rasters (from NLCD for the US (30m resolution) and from Cesbio for France (10m resolution).

Task description

The occurrence dataset will be split in a training set with known species name labels and a test set used for the evaluation. For each occurrence (with geographic images) in the test set, the goal of the task will be to return a candidate set of species with associated confidence scores. The evaluation metrics will be the top-K accuracy (for different values of K) and a set-valued prediction metric to be precised later.

How to participate ?

to be completed soon

Reward

The winner of each of the four LifeCLEF 2020 challenges will be offered a cloud credit grants of 5k USD as part of Microsoft's AI for earth program.
Pl@ntNet