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.

A volcanic eruption initiated on 23/Nov/2014 on Fogo Island in Cape Verde (14.91°N, 24.38°W, Figure 1 ) lasted for 78 days until 08/Feb/2015 [1] – [4] . Volcanic eruptions are phenomena with strong heat fluxes due to the release of lava and gases that can be suitably monitored through thermal remote sensing and its principles [5] . The sensors acquiring such type of data have generally a high temporal resolution but a low spatial one. But at the same time, non-thermal imagery captured by other sensors can also be very useful to better understand the ongoing event and track its temporal evolution [6] . Therefore, we propose a methodology to reconstruct the 2014-2015 Fogo eruption based on multi-platform imagery.