Spatial implicit neural representations for global-scale species mapping
Estimating the geographical range of a species from sparse observations is a challenging
and important geospatial prediction problem. Given a set of locations where a species has …
and important geospatial prediction problem. Given a set of locations where a species has …
Overview of lifeclef 2020: a system-oriented evaluation of automated species identification and species distribution prediction
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 …
species is essential for the sustainable development of humanity, as well as for biodiversity …
[PDF][PDF] Overview of GeoLifeCLEF 2022: Predicting Species Presence from Multi-modal Remote Sensing, Bioclimatic and Pedologic Data.
Understanding the geographic distribution of species is a key concern in conservation. By
pairing species occurrences with environmental features, researchers can model the …
pairing species occurrences with environmental features, researchers can model the …
Very high resolution species distribution modeling based on remote sensing imagery: how to capture fine-grained and large-scale vegetation ecology with …
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 …
geographic distribution of species based on environmental data. They are also very useful …
[PDF][PDF] Overview of GeoLifeCLEF 2021: Predicting species distribution from 2 million remote sensing images.
Understanding the geographic distribution of species is a key concern in conservation. By
pairing species occurrences with environmental features, researchers can model the …
pairing species occurrences with environmental features, researchers can model the …
[PDF][PDF] Contrastive Representation Learning for Natural World Imagery: Habitat prediction for 30, 000 species.
S Seneviratne - CLEF (Working Notes), 2021 - researchgate.net
Recent work in contrastive representation learning has pushed the boundaries of
classification tasks in computer vision, achieving state of the art results on many established …
classification tasks in computer vision, achieving state of the art results on many established …
Uncertainty in predictions of deep learning models for fine-grained classification
T Lorieul - 2020 - theses.hal.science
Deep neural networks have shown dramatic improvements in a lot of supervised
classification tasks. Such models are usually trained with the objective to ultimately minimize …
classification tasks. Such models are usually trained with the objective to ultimately minimize …
Training techniques for presence-only habitat suitability mapping with deep learning
The goal of habitat suitability mapping is to predict the lo-cations in which a given species
could be present. This is typically accomplished by statistical models which use envi …
could be present. This is typically accomplished by statistical models which use envi …
Participation of LIRMM/Inria to the GeoLifeCLEF 2020 challenge
This paper describes the methods that we have implemented in the context of the
GeoLifeCLEF 2020 machine learning challenge. The goal of this challenge is to advance …
GeoLifeCLEF 2020 machine learning challenge. The goal of this challenge is to advance …
Benjamin Deneu 1, 2, 3*, Alexis Joly 1, 2, Pierre Bonnet 3, 4, Maximilien Servajean 2, 5 and François Munoz 6
AC Wiedenhoeft, M Di Febbraro… - … Biodiversity Science in …, 2022 - books.google.com
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 …
geographic distribution of species based on environmental data. They are also very useful …