Abstract
To demonstrate applicable improvements to the urban cadastre of the city of Rio de Janeiro, state-of-the-art techniques for 3D building modeling were investigated, focusing on the geometric detailing of roofs. The experiments subjected data from a previous cadastre to automated processing, using open-source software, to increase the level of detail of pre-existing features, optimizing resources, in a pioneering initiative for large cities in the Global South. The results are useful for various environmental studies, by incorporating new characteristics into urban records, and highlight human intervention as an essential element for ensuring quality.
References
ANGRILL, Sara et al. Urban rainwater runoff quantity and quality – A potential endogenous resource in cities? Journal of Environmental Management, v. 189, p. 14–21, 2017. ISSN 0301-4797. DOI: 10.1016/j.jenvman.2016.12.027.
BILJECKI, Filip et al. Applications of 3D City Models: State of the Art Review. ISPRS International Journal of Geo-Information, v. 4, n. 4, p. 2842–2889, 2015. ISSN 2220-9964. DOI: 10.3390/ijgi4042842.
BILJECKI, Filip; LEDOUX, Hugo; STOTER, Jantien. An improved LOD specification for 3D building models. Computers, environment and urban systems, Elsevier, v. 59, p. 25–37, 2016. DOI: 10.1016/j.compenvurbsys.2016.04.005.
BRASIL. Decreto nº 11.888 de 22 de janeiro de 2024: Estratégia Nacional de Disseminação do Building Information Modelling no Brasil. Brasília, DF: Ministério do Desenvolvimento, Indústria, Comércio e Serviços, 2024.
BRASIL. Lei nº 13.709 de 14 de agosto de 2018: Lei Geral de Proteção de Dados Pessoais (LGPD). Brasília, DF: Presidência da República, 2018.
BUCCOLIERI, Riccardo; HANG, Jian. Recent Advances in Urban Ventilation Assessment and Flow Modelling. Atmosphere, v. 10, n. 3, 2019. ISSN 2073-4433. DOI: 10.3390/atmos10030144.
FALCÃO, Jonatas Goulart M. et al. Unregulated Vertical Urban Growth Alters Microclimate: Coupling Building-Scale Digital Surface Models with High-Resolution Microclimate Simulations. Smart Cities, v. 8, n. 6, 2025. ISSN 2624-6511. DOI: 10.3390/smartcities8060191.
GRAHAM, Lewis. The LAS 1.4 Specification. Photogrammetric Engineering & Remote Sensing, v. 78, n. 2, 2012. ISSN 0099-1112.
GRÖGER, Gerhard; PLÜMER, Lutz. CityGML – Interoperable semantic 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing, v. 71, p. 12–33, 2012. ISSN 0924-2716. DOI: 10.1016/j.isprsjprs.2012.04.004.
GUO, Haoran et al. A method for hierarchical weighted fitting of regular grid DSM with discrete points. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus Publications Göttingen, Germany, v. 10, p. 91–98, 2024. DOI: 10.5194/isprs-annals-X-1-2024-91-2024.
HAO, Lechuan; ZHANG, Ye; CAO, Zhimin. Building extraction from stereo aerial images based on multi-layer line grouping with height constraint. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). [S.l.: s.n.], 2016. P. 445–448. DOI: 10.1109/IGARSS.2016.7729110.
HIRSCHMÜLLER, Heiko. Semi-global matching-motivation, developments and applications. Photogrammetric Week 11, Wichmann, p. 173–184, 2011. Disponível em: <https://elib.dlr.de/73119/>.
ISMAEL, Rojgar Qarani; SADEQ, Haval. LoD2 Building Reconstruction from Stereo Satellite Imagery using Deep Learning and Model-Driven Approach. Zanco Journal of Pure and Applied Sciences, v. 37, n. 2, p. 103–118, 2025. Disponível em: <https://zancojournal.su.edu.krd/index.php/JPAS/article/view/3140>.
KRAPF, S. et al. RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment. Remote Sensing, v. 14, n. 10, 2022a. ISSN 2072-4292. DOI: 10.3390/rs14102299.
KRAPF, S. et al. Deep Learning for Semantic 3D City Model Extension: Modeling Roof Superstructures Using Aerial Images for Solar Potential Analysis. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, v. 10, 4/W2-2022, p. 161–168, out. 2022b. ISSN 2194-9042. DOI: 10.5194/isprs-annals-X-4-W2-2022-161-2022.
LUSSANGE, Johann et al. KIBS: 3D detection of planar roof sections from a single satellite image. ISPRS Journal of Photogrammetry and Remote Sensing, v. 220, p. 207–216, 2025. ISSN 0924-2716. DOI: 10.1016/j.isprsjprs.2024.11.014.
MOHAJERI, Nahid et al. A city-scale roof shape classification using machine learning for solar energy applications. Renewable Energy, v. 121, p. 81–93, 2018. ISSN 0960-1481. DOI: 10.1016/j.renene.2017.12.096.
MOHAMMADI, Hamid; SAMADZADEGAN, Farhad; REINARTZ, Peter. 2D/3D information fusion for building extraction from high-resolution satellite stereo images using kernel graph cuts. International Journal of Remote Sensing, Taylor & Francis, v. 40, n. 15, p. 5835–5860, 2019. DOI: 10.1080/01431161.2019.1584417.
PAIVA, Gabriel M.; BADOLATO, Irving S.; COELHO, Luiz Carlos T. Segmentação de Cobertura Arbórea em Área Urbana Sobre Ortoimagens RGB-NIR. [S.l.]: Zenodo, nov. 2024. DOI: 10.5281/zenodo.14003436.
PETERS, Ravi et al. Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands. Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry e Remote Sensing, v. 88, n. 3, p. 165–170, 2022. DOI: 10.14358/PERS.21-00032R2.
ROBINSON, Darren; STONE, Andrew. Irradiation modelling made simple: the cumulative sky approach and its applications. In: PLEA conference. [S.l.: s.n.], 2004. P. 19–22.
SEILOV, Sh Zh et al. The concept of building a network of digital twins to increase the efficiency of complex telecommunication systems. Complexity, Wiley Online Library, v. 2021, n. 1, p. 9480235, 2021.
STOTER, Jantien et al. Automated reconstruction of 3D input data for noise simulation. Computers, Environment and Urban Systems, v. 80, p. 101424, 2020. ISSN 0198-9715. DOI: 10.1016/j.compenvurbsys.2019.101424.
SUN, Xiaokai et al. Semantic Segmentation and Roof Reconstruction of Urban Buildings Based on LiDAR Point Clouds. ISPRS International Journal of Geo-Information, v. 13, n. 1, 2024. ISSN 2220-9964. DOI: 10.3390/ijgi13010019.
TOPOCART AEROLEVANTAMENTOS. Relatório de execução de mapeamento aerofotogramétrico do município do Rio de Janeiro por mosaicos de ortoimagens digitais coloridas obtidas por plataforma aérea e ortorretificadas de acordo com elevações (“True Ortho”); modelo digital de elevações e modelo digital do terreno por perfilamento a LASER. Brasília, Brasil, 2019.
VERMA, V.; KUMAR, R.; HSU, S. 3D Building Detection and Modeling from Aerial LIDAR Data. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06). [S.l.: s.n.], 2006. v. 2, p. 2213–2220. DOI: 10.1109/CVPR.2006.12.
WANG, Chen; HOU, Jingming et al. Flood risk management in sponge cities: The role of integrated simulation and 3D visualization. International Journal of Disaster Risk Reduction, v. 39, p. 101139, 2019. ISSN 2212-4209. DOI: 10.1016/j.ijdrr.2019.101139.
WANG, Ruisheng. 3D building modeling using images and LiDAR: a review. International Journal of Image and Data Fusion, Taylor & Francis, v. 4, n. 4, p. 273–292, 2013. DOI: 10.1080/19479832.2013.811124.
WANG, Ruisheng; PEETHAMBARAN, Jiju; CHEN, Dong. LiDAR Point Clouds to 3-D Urban Models: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 11, n. 2, p. 606–627, 2018. DOI: 10.1109/JSTARS.2017.2781132.
WYSOCKI, Olaf et al. Reviewing Open Data Semantic 3D City Models to Develop Novel 3D Reconstruction Methods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 48, p. 493–500, 2024. DOI: 10.5194/isprs-archives-XLVIII-4-2024-493-2024.

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Copyright (c) 2026 Irving da Silva Badolato, Guilherme Lucio Abelha Mota, Gilson Alexandre Ostwald Pedro da Costa

