Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction

Abstract

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals in the field is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2020 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: location-based prediction of species based on environmental and occurrence data, and (iv) SnakeCLEF: snake identification based on image and geographic location.

Sharing taxonomic expertise between natural history collections using image recognition

“…However, not all images are appropriate for training as many collections digitize their holdings without prior verification of the taxonomic identifications. Using these images could decrease the quality of the model but can still be used in unsupervised learning (Chen et al 2020, Caron et al 2020). The authors therefore suggest establishing a Central Library of Datasets to access specimen images with high confidence identifications.…”

“…Collaborations between existing infrastructures like GBIF, Catalogue of Life, Zenodo (https://zenodo.org) amongst others, and initiatives such as DiSSCo and iDigBio could provide a framework for the repurpose of existing tooling and infrastructure components and newly developed ones. Having a standardized framework for storing and evaluating algorithms on well described datasets also provides the opportunity for the machine learning research community to compete on creating the best models in the form of challenges (Joly et al 2020, Little et al 2020). In the end, all modules need to fit and work together and be actively and sustainably maintained.…”

“…Learning with unlabeled images is part of the field of unsupervised machine learning. A surge of recent papers(Chen et al 2020, Caron et al 2020) have shown that using a large number (billions) of unlabeled images can reduce the number of labeled images needed to achieve high recognition accuracy. This opens the possibility of making use of the large volumes of unlabeled material that are present in museum collections.…”

Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 Solution

“…The BirdCLEF 2021 Challenge [1,2] proposes to identify bird calls in soundscape recordings. The challenge was hosted on Kaggle from April 1, 2021 to June 1, 2021 1 .…”

Overview of LifeCLEF 2021: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

“…In order to measure progress in a sustainable and repeatable way, the LifeCLEF 2 research platform was created in 2014 as a continuation and extension of the plant identification task that had been run within the ImageCLEF lab 3 since 2011 [14,15,16]. Since 2014, LifeCLEF expanded the challenge by considering animals in addition to plants, and including audio and video content in addition to images [23,24,25,26,27,28,29]. Four challenges were evaluated in the context of LifeCLEF 2021 edition: 1.…”