Automated identification of hedgerows and hedgerow gaps using deep learning

Abstract Hedgerows are a key component of the UK landscape that form boundaries, borders and limits of land whilst providing vital landscape‐scale ecological connectivity for a range of organisms. They are diverse habitats in the agricultural landscape providing a range of ecosystem services. Poorly managed hedgerows often present with gaps, reducing their ecological connectivity, resulting in fragmented habitats. However, hedgerow gap frequency and spatial distributions are often unquantified at the landscape‐scale. Here we present a novel methodology based on deep learning (DL) that is coupled with high‐resolution aerial imagery. We demonstrate how this provides a route towards a rapid, adaptable, accurate assessment of hedgerow and gap abundance at such scales, with minimal training data. We present the training and development of a DL model using the U‐Net architecture to automatically identify hedgerows across the East Riding of Yorkshire (ERY) in the UK and demonstrate the ability of the model to estimate hedgerow gap types, lengths and their locations. Our method was both time efficient and accurate, processing an area of 2479 km2 in 32 h with an overall accuracy of 92.4%. The substantive results allow us to estimate that in the ERY alone, there were 3982 ± 302 km of hedgerows and 2865 ± 217 km of hedgerow gaps (with 339 km classified as for access). Our approach and study show that hedgerows and gaps can be extracted from true colour aerial imagery without the requirement of elevation data and can produce meaningful results that lead to the identification of prioritisation areas for hedgerow gap infilling, replanting and restoration. Such replanting could significantly contribute towards national tree planting goals and meeting net zero targets in a changing climate.