In the age of big data, scientific progress is fundamentally limited by our capacity to extract
critical information. Here, we map fine-grained spatiotemporal distributions for thousands of …

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

Convolutional neural networks (CNNs) have shown outstanding image classification
performance, having been successfully applied in several real‐world applications. However …

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 …

Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary
to ensure food, water, and human health and well-being. Understanding the distribution of …

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 …

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 spatio-temporal distribution of species is a cornerstone of ecology and
conservation. By pairing species observations with geographic and environmental …

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 …

Climate change is a major driver of biodiversity loss, changing the geographic range and
abundance of many species. However, there remain significant knowledge gaps about the …