Hedgerow mapping and detection using deep learning methods in very high resolution remote sensing imagery.
Urbanization and the rapid expansion of cities have resulted in the loss of natural habitats and green spaces, leading to significant environmental challenges. In recent years, there has been a growing recognition of the importance of integrating green infrastructure into urban environments to mitigate the negative impacts of urbanization and promote sustainable development. Hedgerows, traditionally associated with rural landscapes, have emerged as valuable components of urban green spaces due to their multiple ecological benefits. These linear vegetation features, characterized by dense rows of shrubs and trees, provide numerous ecosystem services, including biodiversity conservation, habitat connectivity, microclimate regulation, and aesthetic enhancement. Accurate detection and characterization of hedgerows in urban areas are crucial for effective land management, urban planning, and conservation efforts. Traditional field-based methods for hedgerow detection are time-consuming, labor-intensive, and often limited in scope. However, advancements in remote sensing technologies, particularly in the fields of remote sensing image analysis and deep learning, offer new opportunities to overcome these limitations and enable large-scale hedgerow detection and monitoring. Urban hedgerow detection and characterization face several challenges due to the complex nature of urban landscapes. The presence of occlusions caused by buildings, roads, and other structures can hinder accurate detection and classification. Additionally, the fragmented nature of urban hedgerows, varying growth patterns, and mixed vegetation types present further difficulties. Overcoming these challenges requires advanced remote sensing techniques, innovative feature extraction methods, and the integration of multi-sensor data sources to improve the accuracy and reliability of hedgerow detection and characterization.