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Publications (32)
This paper describes a deep-SDM framework, MALPOLON. Written in Python and built upon the PyTorch library, this framework aims to facilitate training and inferences of deep species distribution models (deep-SDM) and sharing for users with only general Python language skills (e.g., modeling ecologists) who are interested in testing deep learning app...
The difficulty of monitoring biodiversity at fine scales and over large areas limits ecological knowledge and conservation efforts. To fill this gap, Species Distribution Models (SDMs) predict species across space from spatially explicit features. Yet, they face the challenge of integrating the rich but heterogeneous data made available over the pa...
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, species identification and inventory is a difficult and costly task, requiring large-scale automated approaches. The LifeCLEF lab has been...
Human activities have a growing impact on global biodiversity. While our understanding of biodiversity worldwide is not yet comprehensive, it is crucial to explore effective means of characterizing it in order to mitigate these impacts. The advancements in data storage, exchange capabilities, and the increasing availability of extensive taxonomic,...
Biodiversity monitoring through AI approaches is essential, as it enables the efficient analysis of vast amounts of data, providing comprehensive insights into species distribution and ecosystem health and aiding in informed conservation decisions. Species identification based on images and sounds, in particular, is invaluable for facilitating biod...
The difficulty to measure or predict species community composition at fine spatio-temporal resolution and over large spatial scales severely hampers our ability to understand species assemblages and take appropriate conservation measures. Despite the progress in species distribution modeling (SDM) over the past decades, SDM have just begun to integ...
Les modèles de distributions d'espèces font le lien entre la distribution géographique d’une espèce et son environnement. Les objectifs de l’utilisation de ces modèles sont multiples. On peut citer entre autres l’extraction de connaissance sur les espèces et leur préférences environnementales, l’aide aux plans de conservations et politiques de prot...
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, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming livi...
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, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming liv...
Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the geographic distribution of species based on environmental data. They are also very useful from an application point of view, whether for the implementation of conservation plans for threatened species or for monitoring invasive species. The generalizability and s...
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, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming livi...
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 is hindering the aggregation of new data and knowledge. Identifying and naming living plan...
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 is hindering the aggregation of new data and knowledge. Identifying and naming living pla...
Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thu...
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 namin...
Species distribution models (SDM) assess and predict how species spatial distributions depend on the environment, due to species ecological preferences. These models are used in many different scenarios such as conservation plans or monitoring of invasive species. The choice of a model and of environmental data have strong impact on the model’s abi...
Pl@ntNet is a scientific and citizen platform based on artificial intelligence techniques to help participants more easily identify plants with their smartphones. The identification of plant species is indeed an important step for many scientific, educational and land management activities (for natural or cultivated spaces). This step, which is int...
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 namin...
1. Successful monitoring and management of plant resources worldwide needs the involvement of civil society to support natural reserve managers. Because it is difficult to correctly and quickly identify plant species for non-specialists, the development of recent techniques based on automatic visual identification should facilitate and increase pub...
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 namin...
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 nami...
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 dataset, which consists of 1.9...
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 namin...
Pl@ntNet is a scientific and citizen platform based on artificial intelligence techniques to help participants more easily identify plants with their smartphones. The identification of plant species is indeed an important step for many scientific, educational and land management activities (for natural or cultivated spaces). This step, which is int...
This paper presents an evaluation of several approaches of plants species distribution modeling based on spatial, environmental and co-occurrences data using machine learning methods. In particular, we re-evaluate the environmental convolutional neural network model that obtained the best performance of the GeoLifeCLEF 2018 challenge but on a revis...
This paper presents an evaluation of several approaches of plants species distribution modeling based on spatial, environmental and co-occurrences data using machine learning methods. In particular, we re-evaluate the environmental convolutional neural network model that obtained the best performance of the GeoLifeCLEF 2018 challenge but on a revis...
This paper presents several approaches for plant predictions given their location in the context of the GeoLifeCLEF 2018 challenge. We have developed three kinds of prediction models, one convolutional neural network on environmental data (CNN), one neural network on co-occurrences data and two other models only based on the spatial occurrences of...
