\n This year's challenge, GeoLifeCLEF 2021, uses the same data as last year's one, GeoLifeCLEF 2020, with some modifications in the format to make it both smaller (~80Go instead if ~850Go) and easier to use.\n
\n\n\n The challenge relies on a collection of millions of observations of plants and animals in the US and France, coming from the iNaturalist and Pl@ntNet citizen science platforms.
\n\n Each observation consists of a species name with the GPS coordinates where it was observed.
\n\n In addition, observations are paired with a set of covariates characterizing the landscape and environment around them.
\n\n Covariates include high-resolution remote sensing imagery, land cover data, and altitude, as well as traditional low-resolution climate and soil variables.
\n
![\"Observation](\"https://raw.githubusercontent.com/maximiliense/GLC/GLC21/images/distribution_FR.png\")
\n \n![\"Observation](\"https://raw.githubusercontent.com/maximiliense/GLC/GLC21/images/distribution_US.png\")
\n\n\n In more detail, each occurrence is paired with the following covariates:\n
10561900:\n![\"Observation](\"https://raw.githubusercontent.com/maximiliense/GLC/GLC21/images/patches_sample.jpg\")
\n\n\n A workthrough notebook describing the data structure and how to load it and visualize it is available in the Code tab.
\n A complete description of how the dataset was built can be found in the associated paper.
\n A script to download all the data at once as well as data loading and visualization utilities are provided on the GitHub of the competition.\n
\n On this competition page, the observations data, the associated metadata, the rasters and pre-extracted features are provided.
\n Only a subsample of the patches are available here.
\n The complete set of patches can downloaded using those links:\n
![\"Illustration](\"https://raw.githubusercontent.com/maximiliense/GLC/GLC21/images/dataset_illustration.jpg\")
\n\n\n The aim of this competition is to predict the localization of plant and animal species.
\n To do so, 1.9M geo-localized observations from France and the US of 31K species are provided.
\n These observations are paired with aerial images and environmental features around them (as illustrated above).
\n The goal is, for each GPS position in the test set (for which we provide the associated aerial images and environmental features), to return a set of candidate species which should contain the true observed species.\n
\n 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.
\n First, this would allow to improve species identification tools - automatic, semi-automatic or based on traditional field guides - by reducing the list of candidate species observable at a given site.
\n More generally, it 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.
\n Finally, this could be used for educational purposes through biodiversity discovery applications with features such as contextualized educational pathways.\n
\n This competition is held jointly as part of:\n
\n Being part of scientific research, the participants are encouraged to participate to the LifeCLEF lab by registering to it using this form (and checking \"Task 3 - GeoLifeCLEF\" of LifeCLEF).
\n This will allow those participants to submit, after the competition ends, a working note paper to LifeCLEF which will be peer reviewed (CEUR-WS proceedings).
\n This paper should provide sufficient information to reproduce the final submitted runs.
\n Only participants who submitted a working note paper will be part of the officially published ranking used for scientific communication.\n
\n LifeCLEF lab is part of the Conference and Labs of the Evaluation Forum (CLEF).
\n CLEF 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.
\n CLEF 2021 will be hosted by the University \"Politehnica\" of Bucharest, Romania, 21-24 September 2021.
\n More details can be found on the CLEF 2021 website.\n
\n Submitting a working note with the full description of the methods used in each run is mandatory.
\n 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.
\n 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.
\n According to the CEUR-WS policies, a light review of the working notes will be conducted by LifeCLEF organizing committee to ensure quality.
\n As an illustration, LifeCLEF 2020 working notes (task overviews and participant working notes) can be found within CLEF 2020 CEUR-WS proceedings. \n
\n This competition is part of the Fine-Grained Visual Categorization FGVC8 workshop at the Computer Vision and Pattern Recognition Conference CVPR 2021.
\n A panel will review the top submissions for the competition based on the description of the methods provided.
\n From this, a subset may be invited to present their results at the workshop.
\n Attending the workshop is not required to participate in the competition; however, only teams that are attending the workshop will be considered to present their work.\n
\n CVPR 2021 will take place virtually from June 19th to June 25th.
\n PLEASE NOTE: CVPR frequently sells out early, we cannot guarantee CVPR registration after the competition's end.
\n If you are interested in attending, please plan ahead.\n
\n You can see a list of all of the FGVC8 competitions here.\n
\n\n\n The winners will be offered a cloud credit grant of 5k USD as part of Microsoft's AI for Earth program.
\n Furthermore, they will be offered the registration fees for one person to CLEF and CVPR.\n
\n In order to claim the Azure price as well as the offered attendance fees, the winners are required to register to LifeCLEF and submit a working note paper.
\n These prices will be given to the team with highest rank on the private leaderboard who will have matched these requirements.\n
\n See the Rule tab for more details.\n
\n The evaluation metric for this competition is top-30 error rate.
\n Each observation \\( i \\) is associated with a single ground-truth label \\( y_i \\) corresponding to the observed species.
\n For each observation, the submissions will provide 30 candidate labels \\( \\hat{y}_{i,1}, \\hat{y}_{i,2}, \\dots, {\\hat{y}}_{i,30} \\).
\n The top-30 error rate is then computed using\n
\n \\[\n \\text{Top-30 error rate} = \\frac{1}{N} \\sum_{i=1}^N e_i\n \\quad \\text{where} \\quad\n e_i = \\begin{cases}\n 1 & \\text{if } \\, \\forall k \\in \\{1,\\dots,30\\}, \\, \\hat{y}_{i,k} \\neq y_i \\\\\n 0 & \\text{otherwise}\n \\end{cases}\n \\]\n
\n\n\n![\"Illustration](\"https://raw.githubusercontent.com/maximiliense/GLC/GLC21/images/train_test_split.png\")
\n\n\n In order to limit the spatial bias during evaluation, the observations were split into training and test sets using a spatial block holdout procedure.
\n This procedure is illustrated in the previous figure: the test data - in red - is drawn from different spatial blocks than the training data - in blue.\n
\n In more details, all the observations are assigned into a grid of 5km×5km quadrats.
\n 2.5% of these quadrats are randomly sampled for the test set while the remaining ones are used for the training set.\n
\n A validation set built using the same splitting procedure is provided (column subset of observations_*_train.csv).
\n The code for this splitting procedure is also available on the GitHub.\n
\n We provide 3 baselines in the leaderboard based on last year's results:\n
\n We also provide a notebook explaining how to compute some simpler baselines.\n
\n\n\n The submission format is a CSV file containing two columns for each observation:\n
Id column containing integers corresponding to the observations idsPredicted column containing space-delimited lists of the 30 predicted labels made for each observationtest_observation_ids_mapping.csv before submitting.\n\n\n The file should contain a header and have the following format:\n
\nId, Predicted\n1, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n2, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30\n...\n\n\n
\n See also the following notebook showing how submission files can be generated.\n
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "Overview > Timeline page.\n\n\n\n Using external observation data (such as data from GBIF, etc.) is forbidden.
\n Other external data (such as image data) used for model pre-training is authorized as long as it is publicly accessible.
\n In particular, using pre-trained models from standard sources, such as Pytorch hub, torchvision, etc., is authorized.
\n In case of doubt, please contact us at geolifeclef@inria.fr.
\n If such data or pre-trained is used, they must be explicitly referenced in the submitted working note papers (see below).\n
\n In order to be part of the official ranking of GeoLifeCLEF 2021 lab which will be published and used in scientific communication, the teams are required to submit a working note paper to LifeCLEF workshop describing the methods implemented which will be peer reviewed.
\n This working note must provide sufficient details to allow to reproduce all final submissions.
\n 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.
\n
\n 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.
\n 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 2020 working notes (task overviews and participant working notes) can be found within [CLEF 2020 CEUR-WS proceedings] (http://ceur-ws.org/Vol-2696/).
\n\nBelow the link gives more information about the templates (latex, doc) to use, the mandatory information to report (about the names, affiliations, etc):
\nCLEF instructions for authors
\nThe direct link to submit your working note through easychair is:
\nhttps://easychair.org/my/conference?conf=clef2021\n
\n The top-1 team of the official ranking of GeoLifeCLEF 2021 (having submitted a working note paper) will be offered a cloud credit grant of 5k USD as part of Microsoft's AI for Earth program.
\n Furthermore, they will be offered the registration fees for one person to CLEF and CVPR.\n
\n All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted.
\n The competition organizers reserve the right to update the contest timeline if they deem it necessary.\n