DeepVerge: Classification of Roadside Verge Biodiversity and Conservation Potential
This addresses conservation planning for local authorities by automating the identification of wildlife sites, potentially saving manual labor, though it is incremental as it applies existing deep learning to a new environmental domain.
The authors tackled the problem of manually surveying roadside verges for biodiversity by developing DeepVerge, a deep learning method that automatically detects indicator species from street-view imagery, achieving 88% mean accuracy on data from Lincolnshire.
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.