Description Task
Continuously predicting the composition of plant species and its change in space and time at a fine resolution is useful for many scenarios related to biodiversity management and conservation, the improvement of species identification and inventory tools, as well as for educational purposes.
The objective of this challenge is to predict the set of plant species present 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, large-scale.
![graphical abstract](https://filedn.com/ltHjVlFrSfSJL1S4hXqqTzy/GLC_2023_graphical_abstract.jpg)
Motivation
Predicting the set of plant species present at a given location is useful for many scenarios related to biodiversity management and conservation.
First, it allows building high-resolution maps of species composition and of related biodiversity indicators such as species diversity, presence of endangered species, presence of invasive species, etc. In scientific ecology, the problem is known as Species Distribution Modelling.
Moreover, it could allow to significantly improve the accuracy of species identification tools - such as
[Pl@ntNet](https://plantnet.org/)
- by reducing the list of candidate species observable at a given site.
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.
Finally, this could be used for educational purposes through biodiversity exploration applications with features such as quests or contextualized educational pathways.
Context
This competition is held jointly as part of:
the
[LifeCLEF 2023](https://www.imageclef.org/LifeCLEF2023)
lab of the
[CLEF 2023](http://clef2023.clef-initiative.eu)
conference, and of
the
[FGVC10](https://sites.google.com/view/fgvc10)
workshop organized in conjunction with
[CVPR 2023](http://cvpr2023.thecvf.com)
conference.
Being part of scientific research, the participants are encouraged to participate to both events.
In particular, only participants who submitted a working note paper to LifeCLEF (see below) will be part of the officially published ranking used for scientific communication.
LifeCLEF 2023
LifeCLEF lab is part of the Conference and Labs of the Evaluation Forum (
[CLEF](http://www.clef-initiative.eu)
).
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.
CLEF 2023 will take place in Thessaloniki, Greece, 18-21 September 2023.
More details can be found on the
[CLEF 2023 website](http://clef2023.clef-initiative.eu)
.
Participants should register the LifeCLEF 2023 lab using
[this form](http://clef2023-labs-registration.dei.unipd.it/registrationForm.php)
(and checking "Task 3 - GeoLifeCLEF" of "LifeCLEF" section).
This registration is free of charge.
This registration will give you the opportunity to present your results to the CLEF community, during the LifeCLEF session of CLEF 2023, if you win the challenge and submit a working note.
Indeed, participants are required to submit, at the end of the competition, a working note paper to LifeCLEF which will be peer-reviewed and published in
[CEUR-WS proceedings](http://ceur-ws.org)
.
This paper should provide sufficient information to reproduce the final submitted runs.
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 2022 working notes (task overviews and participant working notes) can be found within
[CLEF 2022 CEUR-WS proceedings](https://ceur-ws.org/Vol-3180/)
.
FGVC10 at CVPR 2023
This competition is part of the
Fine-Grained Visual Categorization
[FGVC10](https://sites.google.com/view/fgvc10)
workshop on the 18th June
at the Computer Vision and Pattern Recognition Conference
[CVPR 2023](http://cvpr2023.thecvf.com)
.
A panel will review the top submissions for the competition based on the description of the methods provided.
The results of the task will be presented at the workshop and the contribution of winner team(s) will be highlighted. Attending the workshop is not required to participate in the competition.
CVPR 2023 will take place in Vancouver, CANADA, 18-22 June 2023.
PLEASE NOTE: CVPR frequently sells out early, we cannot guarantee CVPR registration after the competition's end.
If you are interested in attending, please plan ahead.
You can see a list of all of the FGVC10 competitions
[here](https://sites.google.com/view/fgvc10/competitions)
.
Questions can be asked in the discussion or to geolifeclef@inria.fr
Credits
This project has received funding from the European Union’s Horizon research and innovation program under grant agreement No 101060639 (MAMBO project) and No 101060693 (GUARDEN project).
Organizers and contributors
Christophe Botella, INRIA, LIRMM, Montpellier
Benjamin Deneu, INRIA, LIRMM, Montpellier
Diego Marcos, INRIA , Montpellier
Théo Larcher, INRIA, LIRMM, Montpellier
Joachim Estopinan, INRIA, LIRMM, Montpellier
César Leblanc, INRIA, LIRMM, Montpellier
Maximilien Servajean, Université Paul Valéry, LIRMM, Montpellier
Alexis Joly, INRIA, LIRMM, Montpellier