4 citations · 12 across the 11 of their papers we have counts for
6 papers · 1 filter
Applying the maximum entropy principle to neural networks enhances multi-species distribution models
Maxime Ryckewaert, Diego Marcos, Christophe Botella +3
The rapid expansion of citizen science initiatives has led to a significant growth of biodiversity databases, and particularly presence-only (PO) observations. PO data are invaluab…
MALPOLON: A Framework for Deep Species Distribution Modeling
Theo Larcher, Lukas Picek, Benjamin Deneu +3
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…
Fully automatic extraction of morphological traits from the Web: utopia or reality?
Diego Marcos, Robert van de Vlasakker, Ioannis N. Athanasiadis +7
Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem. However, compiling trait informa…
GeoPlant: Spatial Plant Species Prediction Dataset
Lukas Picek, Christophe Botella, Maximilien Servajean +7
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 (S…
Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts
Joaquim Estopinan, Pierre Bonnet, Maximilien Servajean +2
The post-2020 global biodiversity framework needs ambitious, research-based targets. Estimating the accelerated extinction risk due to climate change is critical. The International…
AI-based Mapping of the Conservation Status of Orchid Assemblages at Global Scale
Joaquim Estopinan, Maximilien Servajean, Pierre Bonnet +2
Although increasing threats on biodiversity are now widely recognised, there are no accurate global maps showing whether and where species assemblages are at risk. We hereby assess…