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.
翻译:开放空间草地正日益被耕种或扩大,从而加大了针对路边边缘的养护努力,大约一半的联合王国草地物种可以在联合王国50万公里的公路上找到,约有91个物种受到威胁或濒临受到威胁。因此,仔细管理这些“野生走廊”对于防止物种灭绝和维持草地生境的生物多样性至关重要。野生动物信托基金经常争取志愿者的支持,调查路边边缘,并确定新的“本地野生生物地点”为具有高度保护潜力的地区。利用3 900公里路边的志愿人员调查数据以及公开提供的街头景象,我们展示了DeepVerge;一种基于深层次学习的方法,通过发现积极指标物种的存在,可以自动测量路边的几处。利用林肯郡乡村县的图像和地面真相调查数据,DeepVerge实现了88 %的平均值。地方当局可以使用这种方法查明新的本地野生生物地点,并根据法律和政府的政策义务进行援助管理和环境规划,节省数千小时的体力劳动。