GeoLifeCLEF 2020


Understanding the geographic distribution of species is a key concern in conservation. By pairing species occurrences with environmental features, researchers can model the relationship between an environment and the species which may be found there. To facilitate research in this area, we present the GeoLifeCLEF 2020 (GLC2020) dataset, which consists of 1.9 million geo-located species observations paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional low-resolution climate and soil variables. The observations in this dataset cover 31,435 plant and animal species from the United States and France. The dataset was originally prepared for the GeoLifeCLEF 2020 competition. Full details can be found in the dataset paper (cited below).


If you use this data set, please cite the associated manuscript:

Cole E, Deneu B, Lorieul T, Servajean M, Botella C, Morris D, Jojic N, Bonnet P, Joly A. The GeoLifeCLEF 2020 Dataset. arXiv preprint arXiv:2004.04192. 2020 Apr 8. (bibtex)

Data format

The dataset consists of three components: high-resolution patches, annotations for those patches, and low-resolution covariate rasters.

Each high-resolution patch is stored as a pair of .npy files: XXX.npy containing RGB-IR and land cover (256x256x5 uint8 array) and XXX_alti.npy containing altitude (256x256x1 uint16 array).

Annotations for the high-resolution patches are provided as JSON files that adhere to a modified version of the COCO dataset annotation format. The format is as follows:

	"images": [image],
	"categories": [category],
	"annotations": [annotation]

	"id": int,
	"width": int,
	"height": int,
	"file_name": str,
	"file_name_alti": str,
	"lon": float,
	"lat": float,
	"country": str

	"id": int,
	"gbif_id": int,
	"gbif_name": str

	"id": int,
	"image_id": int,
	"category_id" int

The low-resolution covariate rasters are provided as pairs of .tif files (one for the US and one for France) for each variable. On the competition GitHub page we provide code to extract values for 27 environmental characteristics at any location in the US or France.

Download links


High-resolution patches are available at:[region]_[index].tar.gz (11GB each)

…where region is “us” or “fr” for the US and France, respectively, and “index” is 01, 02, … 20. For example:

For each country, each of the 20 .tar.gz files contains five directories from the set {00/, …, 99/}. For example, patches_fr_01.tar.gz contains 00/, …, 04/ and patches_fr_02.tar.gz contains 05/, …, 09/, and so on. Then each of those directories contains subdirectories 00/, …, 99/. These indices do not have a semantic interpretation, they are just to help the operating system deal with the number of files. Each pair of patch files is named XXXXABCD.npy and XXXXABCD_alti.npy, and lives at CD/AB/ where CD and AB are each among 00/, …, 99/.

The contents of each file should be extracted to the same directory, such that, for example, the 00/ folder contains the contents of both patches_us_01/ and patches_fr_01/.


Low-resolution covariate rasters

Rasters (2.4GB)

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Contact information

For questions about this dataset, contact


RGB-IR imagery


License: public domain


Source: IGN
License: CC BY 4.0 (By Permission of IGN)

Land cover


License: public domain


Source: CESBIO
License: ODC-BY 1.0

Species occurrences


Source: iNaturalist
License: CC BY-NC 4.0


Source #1: Pl@ntNet and iNaturalist
License #1: CC BY 4.0
Source #2: iNaturalist
License #2: CC BY-NC 4.0


License: public domain

Soil rasters

Source: SoilGrids
License: CC BY 4.0

Bioclimatic rasters

Source: WorldClim
License: CC BY-SA 4.0

Posted by Dan Morris.