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 …

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 …

Spatial representation learning (SRL) aims at learning general-purpose neural network
representations from various types of spatial data (eg, points, polylines, polygons, networks …

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) …

Understanding the geographic distribution of species is a key concern in conservation. By
pairing species occurrences with environmental features, researchers can model the …

Automatic prediction of the plant and animal species most likely to be observed at a given
geo-location is useful for many scenarios related to biodiversity management and …

Species distribution models (SDMs) are critical in conservation decision-making and
ecological or biogeographical inference. Accurately predicting species distribution can …

We present a robust classification approach for avian vocalization in complex and diverse
soundscapes, achieving second place in the BirdCLEF2021 challenge. We illustrate how to …

We focus on the problem of species distribution modeling using global-scale presence-only
data. Most previous studies have mapped the range of a given species using geographical …

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 …