LifeCLEF 2020 Geo
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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.
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.
The challenge relies on a collection of millions of occurrences of plants and animals in the US and France, coming from iNaturalist for the US and from Pl@ntNet for France. 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 (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). *** As soon as the data is released it will be available under the “Resources” tab. ***
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The evaluation criterion will be an adaptive top-K accuracy. For each submission, we will first compute the threshold t of the confidence score that leads to keep K results on average over all test samples (yet each sample may be associated to a different number of predictions). Then, we will compute the pourcentage of test samples for which the correct species is in the kept results.
K will be fixed to 30, which corresponds to the average observed plant species richness across the inventoried plots of the French botanical data of Sophy .
LifeCLEF lab is part of the Conference and Labs of the Evaluation Forum: CLEF 2020. CLEF 2020 consists of independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here.
Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by LifeCLEF organizing committee to ensure quality. As an illustration, LifeCLEF 2019 working notes (task overviews and participant working notes) can be found within CLEF 2019 CEUR-WS proceedings.
Participants of this challenge will automatically be registered at CLEF 2020. In order to be compliant with the CLEF registration requirements, please edit your profile by providing the following additional information:
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Information will be posted after the challenge ends.
The winner of each of the challenge will be offered a cloud credit grant of 5k USD as part of Microsoft’s AI for earth program.
LifeCLEF 2020 is an evaluation campaign that is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings (CEUR-WS.org). Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.
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You can find additional information on the challenge here: https://www.imageclef.org/GeoLifeCLEF2020
 Ruffray, P., B.H.G.r.G.H.M.: “sophy”, une banque de données phytosociologiques; son intérêt pour la conservation de la nature. Actes du colloque “Plantes sauvages et menacées de France: bilan et protection”, Brest, 8-10 octobre 1987 pp. 129–150 (1989).