DeepVerge: Classification of Roadside Verge Biodiversity and Conservation Potential

TL;DR

DeepVerge is a deep learning tool that automatically detects key indicator species in roadside verge images, aiding conservation efforts and reducing manual survey labor.

Contribution

This paper introduces DeepVerge, a novel deep learning approach that uses street-view imagery and volunteer survey data to identify biodiversity hotspots along roads.

Findings

Achieved 88% accuracy in detecting indicator species.

Can process 3,900 km of roadside verges efficiently.

Supports conservation planning and legal compliance.

Abstract

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.