Mapping Green Roofs on Buildings Using Vegetation Indices and Deep Learning Techniques

The identification of strategies to mitigate climate change and address urban challenges is nowadays a priority for urban planners. The installation of green roofs (GR), as a natural-based solution, is widely promoted. Despite this recognition, most installations result from individual initiatives, and their mapping and monitoring remains absent. Over time, the installation of green roofs has followed the building construction sector, moving from individual to groups of buildings organ, grouped in condominiums, on which common shared areas at ground level are covered with GR. The identification of those GRs is important, as they represent the majority of the GR installations in urban areas; however, this task is still very challenging due to the lack of information about the condominium boundaries. This work proposes a methodology for mapping GR at a top and ground level, and monitoring them, through the use of Support Vector Machine classification process, deep learning models, and GIS-based spatial analysis. Applied to the Lisbon Municipality, the methodology enabled the identification and validation of 196 GR. The results demonstrate the effectiveness and scalability of the proposed approach, which surpasses existing methods and is adaptable to diverse urban contexts without reliance on location-specific characteristics.