High‐resolution monitoring is fundamental to understand ecosystems dynamics in an era of
global change and biodiversity declines. While real‐time and automated monitoring of …

Deep learning is an effective machine learning method that in recent years has been
successfully applied to detect and monitor species population in remotely sensed data. This …

Rapid growth of human populations in sub-Saharan Africa has led to a simultaneous
increase in the number of livestock, often leading to conflicts of use with wildlife in protected …

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to
enhance our understanding of animal behaviour and conservation strategies. Using …

Landscape connectivity operates at a variety of scales, depending on the geography of the
area in question and the focal species or ecological process under consideration. Most …

Wildlife surveys are key to assessing the health of global biodiversity. Traditional field and
aerial methods however have significant limitations, including high costs, substantial time …

We introduce WildlifeMapper (WM) a flexible model designed to detect locate and identify
multiple species in aerial imagery. It addresses the limitations of traditional labor-intensive …

Giraffe (Giraffa spp.) numbers and their habitat have drastically declined throughout Africa
over the last century due to various threats linked to anthropogenic impacts including habitat …

Transportation networks can be a major impediment to wildlife movements. We assessed
the use of wildlife underpasses and culverts along a newly constructed railway in Kenya's …

Large African mammal populations are traditionally estimated using the systematic
reconnaissance flights (SRF) with rear-seat observers (RSOs). The oblique-camera-count …