2022/06/09 by Andrew Perrett, Perrett, Andrew, Charlie Barnes +9 · 1 citation
Computer Science · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.4 #Species Distribution and Climate Change #Wildlife Ecology and Conservation #Wildlife-Road Interactions and Conservation #cs.CV
paper · pdf · doi:10.48550/arxiv.2206.04271
arxiv created 2022/06/09 · openalex publication_date 2022/06/09 · arxiv updated 2022/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Open space grassland is being increasingly farmed or built upon, leading to a ramping up of conservation efforts targeting roadside verges. Approximately half of all UK grassland species can be found along the country's 500,000 km of roads, with some 91 species either threatened or near threatened. Careful management of these "wildlife corridors" is therefore essential to preventing species extinction and maintaining biodiversity in grassland habitats. Wildlife trusts have often enlisted the support of volunteers to survey roadside verges and identify new "Local Wildlife Sites" as areas of high conservation potential. Using volunteer survey data from 3,900 km of roadside verges alongside publicly available street-view imagery, we present DeepVerge; a deep learning-based method that can automatically survey sections of roadside verges by detecting the presence of positive indicator species. Using images and ground truth survey data from the rural county of Lincolnshire, DeepVerge achieved a mean accuracy of 88%. Such a method may be used by local authorities to identify new local wildlife sites, and aid management and environmental planning in line with legal and government policy obligations, saving thousands of hours of manual labour.