UrbanFormAnalyzer: an open-source workflow to derive orientation-specific building envelope attributes from cadastral data

Abstract

Detailed building-envelope information is essential for robust urban-scale energy performance analyses. However, national energy certificate databases in Spain and other countries often lack key attributes such as orientation-specific external perimeter measurements and glazing ratios. This paper introduces UrbanFormAnalyzer, an open-source Python library developed with QGIS, which automatically derives orientation-specific external perimeters and additional envelope descriptors from cadastral geometries. The workflow establishes a standard input structure to support integration with varied cadastral sources. As an example, we offer an adapter for the Spanish Cadastre that downloads and preprocesses their KML files, converting them into the required format. The spatial processing stage then calculates the perimeters by cardinal orientation, distinguishing exposed from adjacent walls. To illustrate a potential downstream application, the enhanced datasets are matched with national energy certificates to explore the estimation of orientation-specific glazing ratios, where such data are available. While predictive accuracy remains limited, the case study highlights how UrbanFormAnalyzer could support the creation of reproducible and harmonized building envelope data suitable for a range of analytical applications, including large-scale energy modeling, planning, and renovation assessment.

Keywords

Building envelopes / External perimeters / Cadastral data / QGIS / Energy performance certificates / Glazing ratios

References

[1] Ahmed W, Al-Ramadan B, Asif M, Adamu Z. A gis-based top-down approach to support energy retrofitting for smart urban neighborhoods. Buildings, 2024, 14(3809

[2] Ali, U., Shamsi, M. H., Bohacek, M., Purcell, K., Hoare, C., Mangina, E., & O’Donnell, J. (2020). A data-driven approach for multi-scale gis-based building energy modeling for analysis, planning and support decision making. Applied Energy,279, 115834. https://doi.org/10.1016/j.apenergy.2020.115834

[3] Andersson B, Place W, Kammerud R, Scofield MP. The impact of building orientation on residential heating and cooling. Energy and Buildings, 1985, 8(3): 205-224

[4] Arowoiya VA, Onososen AO, Moehler RC, Fang Y. Influence of thermal comfort on energy consumption for building occupants: The current state of the art. Buildings, 2024, 14(5): 1310

[5] Bacharidis, K., Sarri, F., & Ragia, L. (2020). 3d building façade reconstruction using deep learning. ISPRS International Journal of Geo-Information, 9(5). https://doi.org/10.3390/ijgi9050322

[6] Baset A, Jradi M. Data-driven decision support for smart and efficient building energy retrofits: A review. Applied System Innovation, 2024, 8(1): 5

[7] Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32

[8] Hamilton, C. (2025). Kml tools plugin for qgis. https://plugins.qgis.org/plugins/kmltools/. Accessed 28 Nov 2025.

[9] Chen L, Zheng Y, Yu J, Peng Y, Li R, Han S. A gis-based approach for urban building energy modeling under climate change with high spatial and temporal resolution. Energies, 2024, 17(17): 4313

[10] Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., & Zhou, T. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4), 1–4.

[11] Cherier, M. K., Hamdani, M., Kamel, E., Guermoui, M., Bekkouche, S. M. E. A., Al-Saadi, S., Djeffal, R., Bashir, M. O., Elshekh, A. E., Drozdova, L., Kanaan, M., & Flah, A. (2024). Impact of glazing type, window-to-wall ratio, and orientation on building energy savings quality: A parametric analysis in algerian climatic conditions. Case Studies in Thermal Engineering,61, 104902. https://doi.org/10.1016/j.csite.2024.104902

[12] Comunidad de Madrid (2025). Madrid’s energy performance certificates registry. https://datos.comunidad.madrid/dataset/registro_certificados_eficiencia_energetica. Accessed 28 Nov 2025.

[13] Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297

[14] Deb, C., & Schlueter, A. (2021). Review of data-driven energy modelling techniques for building retrofit. Renewable and Sustainable Energy Reviews,144, 110990. https://doi.org/10.1016/j.rser.2021.110990

[15] Elghamry R, Hassan H. Impact of window parameters on the building envelope on the thermal comfort, energy consumption and cost and environment. International Journal of Ventilation, 2020, 19(4): 233-259

[16] Elhadad S, Orban Z. A sensitivity analysis for thermal performance of building envelope design parameters. Sustainability, 2021, 13(24): 14018

[17] Energistyrelsen. (2025). Denmark’s energy performance certificates registry. https://sparenergi.dk/privat/spar-energi-med-boligens-energimaerke. Accessed 28 Nov 2025.

[18] Fereidoni, S., Nabisi, M., Fereidooni, L., Javidmehr, M., Zirak, N., & Kasaeian, A. (2023). An assessment of the impact of building envelope design on the tradeoff between embodied and operating energy. Energy and Buildings,298, 113542. https://doi.org/10.1016/j.enbuild.2023.113542

[19] Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.

[20] Gobierno de España (2025). Spanish cadastral datasets. https://www.sedecatastro.gob.es/Accesos/SECAccDescargaDatos.aspx. Accessed 28 Nov 2025.

[21] Jeon J, Lee J, Ham Y. Quantifying the impact of building envelope condition on energy use. Building Research & Information, 2019, 47(4): 404-420

[22] Johari, F., Shadram, F., & Widén, J. (2023). Urban building energy modeling from geo-referenced energy performance certificate data: Development, calibration, and validation. Sustainable Cities and Society,96, 104664. https://doi.org/10.1016/j.scs.2023.104664

[23] de Andalucía, J. (2025). Andalusia’s energy performance certificates registry. https://datos.gob.es/gl/catalogo/a01002820-registro-de-certificados-energeticos-de-andalucia. Accessed 28 Nov 2025.

[24] Krüger E, Pearlmutter D, Rasia F. Evaluating the impact of canyon geometry and orientation on cooling loads in a high-mass building in a hot dry environment. Applied Energy, 2010, 87(6): 2068-2078

[25] Li M, Rottensteiner F, Heipke C. Modelling of buildings from aerial lidar point clouds using tins and label maps. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 154: 127-138

[26] Li X, Wu Y. A review of complex window-glazing systems for building energy saving and daylight comfort: Glazing technologies and their building performance prediction. Journal of Building Physics, 2025, 48(4): 496-540

[27] Loga, T., Stein, B., & Diefenbach, N. (2016). Tabula building typologies in 20 European countries–making energy-related features of residential building stocks comparable. Energy and Buildings,132, 4–12. https://doi.org/10.1016/j.enbuild.2016.06.094

[28] Lu, H., Cheng, F., Ma, X., & Hu, G. (2020). Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower. Energy,203, 117756. https://doi.org/10.1016/j.energy.2020.117756

[29] Maanmittauslaitos. (2025). Finnish cadastral datasets. https://asiointi.maanmittauslaitos.fi/karttapaikka/tiedostopalvelu?lang=fi. Accessed 28 Nov 2025.

[30] Ministère de la Transition écologique. (2025). France’s energy performance certificates registry. https://diagnostiqueurs.din.developpement-durable.gouv.fr/index.action. Accessed 28 Nov 2025.

[31] Mirrahimi S, Mohamed MF, Haw LC, Ibrahim NLN, Yusoff WFM, Aflaki A. The effect of building envelope on the thermal comfort and energy saving for high-rise buildings in hot-humid climate. Renewable and Sustainable Energy Reviews, 2016, 53: 1508-1519

[32] Nederlandse Overheid. (2025). Dutch cadastral datasets. https://data.overheid.nl/en/dataset/14284-kadastrale-percelen--inspire-geharmoniseerd-. Accessed 28 Nov 2025.

[33] Nie P, Roccotelli M, Fanti MP, Ming Z, Li Z. Prediction of home energy consumption based on gradient boosting regression tree. Energy Reports, 2021, 7: 1246-1255

[34] Niemann, P., & Schmitz, G. (2020). Impacts of occupancy on energy demand and thermal comfort for a large-sized administration building. Building and Environment,182, 107027. https://doi.org/10.1016/j.buildenv.2020.107027

[35] Peng, J., Curcija, D. C., Thanachareonkit, A., Lee, E. S., Goudey, H., & Selkowitz, S. E. (2019). Study on the overall energy performance of a novel c-si based semitransparent solar photovoltaic window. Applied Energy, 242, 854–872. https://doi.org/10.1016/j.apenergy.2019.03.107

[36] QGIS Development Team. (2025). Qgis geographic information system. https://qgis.org. Accessed 28 Nov 2025.

[37] République Française. (2025). French cadastral datasets. https://cadastre.data.gouv.fr/datasets. Accessed 28 Nov 2025.

[38] Sauer J, Mariani VC, dos Santos Coelho L, Ribeiro MHDM, Rampazzo M. Extreme gradient boosting model based on improved jaya optimizer applied to forecasting energy consumption in residential buildings. Evolving Systems, 2022, 13(4): 577-588

[39] Semple, S., & Jenkins, D. (2020). Variation of energy performance certificate assessments in the European union. Energy Policy,137, 111127. https://doi.org/10.1016/j.enpol.2019.111127

[40] Taud, H., & Mas, J.-F. (2017). Multilayer perceptron (mlp). In Geomatic approaches for modeling land change scenarios (pp. 451–455). Springer. https://doi.org/10.1007/978-3-319-60801-3_27

[41] Touzani S, Granderson J, Fernandes S. Gradient boosting machine for modeling the energy consumption of commercial buildings. Energy and Buildings, 2018, 158: 1533-1543

[42] United Kingdom’s Government (2025). United kingdom’s energy performance certificates registry. https://www.gov.uk/find-energy-certificate. Accessed 28 Nov 2025.

[43] Wang R. 3d building modeling using images and lidar: A review. International Journal of Image and Data Fusion, 2013, 4(4): 273-292

[44] Xunta de Galicia (2025). Galicia’s energy performance certificates registry. https://appsinega.xunta.es/rgeee/web/consultaPublica.php. Accessed 28 Nov 2025.

[45] Yousefi F, Gholipour Y, Yan W. A study of the impact of occupant behaviors on energy performance of building envelopes using occupants’ data. Energy and Buildings, 2017, 148: 182-198

[46] Zhang Y, Zhang C, Chen S, Chen X. Automatic reconstruction of building façade model from photogrammetric mesh model. Remote Sensing, 2021, 13(19): 3801

Funding

Ministry for Digital Transformation and Civil Service and “NextGenerationEU”/PRTR(TSI-100925-2023-1)

Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia and FEDER Galicia 2021-27 operational program(ED431G 2023/01)

GAIN/Xunta de Galicia(ED431C 2025/34)

MCIN/AEI/10.13039/501100011033 and EU/ERDF A way of making Europe(PID2022- 141027NB-C21)

Rights & permissions

The Author(s)