This project addresses the environmental challenges posed by urbanization, particularly the loss of natural habitats and green spaces, by exploring the integration of green infrastructure into urban and non-urban landscapes. These dense rows of shrubs and trees provide essential ecosystem services such as biodiversity conservation, habitat connectivity, microclimate regulation, and aesthetic enhancement. Accurately detecting and characterizing hedgerows in urban areas is vital for effective land management, urban planning, and conservation. Traditional methods for hedgerow detection are often time-consuming and limited in scope, but advancements in remote sensing technologies, including image analysis and deep learning, offer promising solutions. However, urban hedgerow detection is challenging due to the complexity of urban landscapes, with occlusions from buildings and other structures, as well as the fragmented nature and varying growth patterns of urban vegetation. Overcoming these challenges necessitates the use of advanced remote sensing techniques, innovative feature extraction methods, and the integration of multi-sensor data sources, enabling more accurate and reliable hedgerow detection and characterization in urban environments.