mlsanityData to 7 Oct 2026

DeepVerge: Classification of Roadside Verge Biodiversity and Conservation Potential

arXiv 2206.04271 ↗First posted 9 Jun 2022cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 48 references that are in the map. First posted on arXiv 9 Jun 2022.

Builds on

10 of 48 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  6. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 368
  7. 2015Recent Advances in Convolutional Neural Networkscited by 360
  8. 2014CNN Features off-the-shelf: an Astounding Baseline for Recognitioncited by 316
  9. 2014Do Convnets Learn Correspondence?cited by 28
  10. 2015Understanding Intra-Class Knowledge Inside CNNcited by 22

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.