In the Global South, large urban spaces resulted in the duality between the so-called ‘formal’ and ‘informal’ cities. It is the case of São Paulo, a twenty-two-million people metropolis and a financial hub in Latin America. Albeit a vast literature addresses the social-spatial segregation emerging from this dual built environment, the scarcity of spatial datasets regarding informal settlements also enforces a geo-information segregation, resulting in a terra incognita. This is exemplar in favelas, defined as precarious, spontaneous, and unorganised land occupation built on third-party property, most of which lack cadastral data. Since favelas are often not mapped, assessing urban phenomena becomes a technical challenge for several application domains, e.g., the energy one. A recent public initiative in Brazil estimates solar irradiation and photovoltaic potential for buildings at city scale, but favelas are intentionally excluded from the resulting web-based solar maps. Technicians believe that the absence of a spatial pattern in favelas calls for investigation on how to refine a roof mapping methodology. The main research question becomes: “How far is it possible to perform solar analysis on buildings of favelas in São Paulo, with the goal of estimating PV Potential?”. The research is structured into two topics: 1) Roof Mapping, which investigates the data pipeline that leads to a digital reconstruction of favelas; 2) Solar Irradiation, which investigates how existing solar irradiation modules – GRASS GIS, ArcGIS, CitySim, SimStadt, Ladybug and the one developed by Virtual City Systems – perform when assessing buildings of favelas. From a roof mapping perspective, the experiments reveal that the absence of cadastral datasets represents a complex technical challenge. Nevertheless, the reconstructed and post-processed building footprints cover the extension of the cadastral footprints that are available for the study area, with building shapes that are satisfactory as a first approximation. Regarding the solar irradiation perspective, qualitative and quantitative analyses are carried out to compare the results coming from the six solar modules. The qualitative analysis indicates that each solar module offers potentialities but also limitations. Therefore, a straightforward choice is not possible, since the optimal solution will be derived from a data-driven approach that considers, among other factors: the scale of the favela, its topographical characteristics, the presence/absence of urban features other than buildings (such as vegetation), a possible pre-selection of buildings of interest, etc.; The quantitative analysis reveals that ArcGIS outputs an annual summation of irradiation values that is the closest to the one offered by the meteorological station of São Paulo, adopted as ground truth. Nevertheless, from an accuracy perspective, CitySim outputs a daily curve that best corresponds to the ground truth one. In conclusion, based on the geometrical model and the weather dataset criteria, the author expresses his preference for a raster-based solar module – GRASS GIS or ArcGIS – if, on the one hand, the reconstructed building footprints result in an unrealistic or excessively complex vector-based model. On the other hand, if the resulting vector-based model is simple enough and representative of the built environment of the favela, the author suggests the adoption of CitySim.

Noise simulations require finding the paths between multiple receiver and source points. In the current approach, only 3D polylines can be used as input to describe the terrain. These 3D polylines are semi-automatically generated, based on the principle of describing the terrain profile with as few height lines as possible. In order to propose a more efficient, standardised and economic modelling approach, a partnership between RIVM/RWS and the 3D Geoinformation Group at TU Delft was launched in 2017, aiming to generate these height lines automatically from the available datasets, namely AHN3, BAG, and BGT, which are publicly available via PDOK for free. However, it was then proposed to prove that the paths between receiver and source points can be directly generated from a TIN without creating the height lines. The following report provides proof of concept to the hypothesis: ‘Using a TIN directly allows automated 3D noise modelling according to the guidelines of CNOSSOS-EU’. A code was written to generate the paths between receiver and source points using an LoD2 TIN. The paths were then checked visually and were fed to test_Cnossos software to prove their validity. Finally, noise maps were generated and compared to noise maps generated with the current method.