Integrated Geospatial Database of Land Cover, Building Morphology, Local Climate Zones, and ENVI-met Material Parameters for Viçosa, Brazil

This repository contains an integrated geospatial database for the municipality of Viçosa, Minas Gerais, Brazil, developed to represent land cover, urban surfaces, and building morphology. The dataset is designed for use in Geographic Information Systems (GIS) and urban microclimate modeling workflows, with direct support for model parameterization in ENVI-met.

The database harmonizes and integrates multiple public and institutional data sources, including the Brazilian National Water and Sanitation Agency (ANA), municipal Master Plan cartographic annexes, CBERS-4A satellite imagery from the National Institute for Space Research (INPE), MapBiomas Collection 4, Google Open Buildings, the Local Climate Zone (LCZ) Generator, and the Brazilian Institute of Geography and Statistics (IBGE) 2022 Urbanized Areas dataset. Heterogeneous thematic classification schemes are converted into a standardized, high-resolution spatial representation.

Dataset highlights

• Multi-source harmonization: Spatially explicit and standardized representation of major urban and rural surface types on a regular metric grid with a spatial resolution of 5 m (SIRGAS 2000 / UTM zone 23S, EPSG:31983).

• Ready for microclimate modeling: Integrates horizontal surface composition with three-dimensional building morphology and building material parameters to support input preparation for ENVI-met and other environmental models.

• LCZ-enriched building attributes: The building vector dataset associates Google Open Buildings footprints with Local Climate Zones, characteristic building heights, and identifiers for ENVI-met construction materials and thermophysical properties.

• Traceability and reproducibility: Includes lookup tables and automated Python scripts documenting the reclassification and priority-based data fusion procedures.

• Multi-scale coverage: Includes products covering the entire municipality (299.538 km²) and a spatial subset restricted to the IBGE 2022 Urbanized Areas (19.272 km²).

File descriptions

Raster products:

• land_cover_with_buildings.tif- Municipal land cover raster, including a dedicated building footprint class.

• land_cover_without_buildings.tif- Municipal land cover raster in which building footprints are represented as impervious surfaces.

• urbanized_area_2022.tif-Raster clipped to the IBGE 2022 Urbanized Areas, with a NoData mask outside the defined urban extent.

Building footprints:

• buildings_lcz.gpkg- Building footprint vector layer enriched with LCZ classification, assigned building heights, and ENVI-met material identifiers.

Styles: 

• Style_QGIS.txt- Mapping Style for QGIS and dictionary of land cover classes.

Statistical summaries:

 raster_dataset_statistics.xls-XLS files containing area statistics and pixel proportions.

Final raster product specifications

All raster products use the SIRGAS 2000 / UTM zone 23S projected coordinate reference system (EPSG:31983), with a spatial resolution of 5 m × 5 m and a pixel area of 25 m².

Land cover classification dictionary

The harmonized classification scheme comprises the following categories:

Building morphology and material parameterization

The building layer (edificacoes_LCZ.gpkg) was derived from Google Open Buildings and enriched with Local Climate Zone classifications. Each building was assigned an estimated height and a construction material profile compatible with ENVI-met.

1. LCZ-based building height assignment

The following building heights were assigned according to LCZ class:

• LCZ 1 and 4: 30.0 m

• LCZ 2 and 5: 17.5 m

• LCZ 11: 15.0 m

• LCZ 10: 10.0 m

• LCZ 12: 9.0 m

• LCZ 3, 6, 8, and 9: 6.5 m

• LCZ 7, 13, 14, 15, 16, and 17: 3.0 m

These values represent assigned characteristic heights based on LCZ classes rather than individually measured heights for every building.

2. ENVI-met material grouping

The building material lookup table includes the following material groups and properties:

The assigned material properties are intended to support consistent model parameterization. Users should consult the lookup tables and technical documentation before modifying these values or applying them to other urban contexts.

Integrated data sources and processing functions

The dataset integrates these sources into a unified classification and spatial framework. Original source classifications are harmonized according to the lookup tables and processing rules provided with the repository.

Recommended applications

This database can support:

• Urban microclimate modeling: Preparation of three-dimensional simulation domains and surface inputs for ENVI-met.

• Urban heat island studies: Analysis of relationships between built morphology, building envelope materials, and urban thermal patterns.

• Outdoor thermal comfort assessments: Evaluation of indices such as Physiological Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI) at neighborhood or municipal scales.

• Urban planning and climate resilience: Environmental assessments supporting Master Plan development and simulations of urban expansion scenarios.

• GIS-based urban analysis: Spatial assessment of land cover composition, building footprints, and LCZ-associated morphology.

Coordinate reference system and spatial resolution

• Coordinate reference system: SIRGAS 2000 / UTM zone 23S.

• EPSG code: 31983.

• Raster resolution: 5 m × 5 m.

• Pixel area: 25 m².

• Geographic coverage: Municipality of Viçosa, Minas Gerais, Brazil, including a dedicated subset corresponding to the IBGE 2022 Urbanized Areas.

Data limitations and considerations

Building heights are assigned from LCZ-based characteristic values and should not be interpreted as individually measured building heights. Similarly, material identifiers and thermophysical properties represent standardized modeling assumptions rather than verified measurements of every building's actual construction materials.

The spatial accuracy and thematic detail of the integrated products depend on the original datasets, their dates, spatial resolutions, and classification procedures. Users should consult the lookup tables, scripts, and accompanying documentation when adapting the database to other areas or modeling conditions.

The raster products contain NoData cells outside their respective valid spatial extents. In particular, urbanized_area_2022.tif is restricted to the IBGE 2022 Urbanized Areas mask and therefore has a high proportion of NoData cells relative to its rectangular raster dimensions.

Recommended citation

Users are also encouraged to cite the original data sources and relevant methodological publications corresponding to the products they use, following the citation instructions provided by the respective data providers.

Agência Nacional de Águas. (2016, April 7). Uso e ocupação do solo em alta resolução espacial (1 m) da zona urbana dos municípios com mais de 50.000 habitantes na Bacia do Rio Doce. Sistema Nacional de Informações sobre Recursos Hídricos. https://metadados.snirh.gov.br/geonetwork/srv/api/records/7e4929ae-a1a3-4800-8e0f-b111835d8295

Bortoli, G. (2026). WUDAPT Level 0 training data for Viçosa (Brazil, Federative Republic), submitted to the LCZ Generator. WUDAPT / LCZ Generator. https://lcz-generator.rub.de/factsheets/5ed17a5103bcc12ffc4242e9f59f80e304b2a512/5ed17a5103bcc12ffc4242e9f59f80e304b2a512_factsheet.html

Câmara Municipal de Viçosa. (n.d.). Plano Diretor de Viçosa: Anexos 3 e 4. https://www.vicosa.mg.leg.br/processo-legislativo/plano-diretor

Google Research. (2023). Open Buildings dataset (Version 3). https://sites.research.google/open-buildings/

Instituto Brasileiro de Geografia e Estatística. (2022). Áreas urbanizadas do Brasil 2022. https://www.ibge.gov.br/geociencias/cartas-e-mapas/redes-geograficas/15789-areas-urbanizadas.html

Instituto Nacional de Pesquisas Espaciais. (2026, September 17). CBERS-4A: Imagens do sensor MUX. Programa CBERS. https://www.gov.br/inpe/pt-br

MapBiomas. (2026, September 18). Coleção 11 da Série Anual de Mapas de Cobertura e Uso da Terra do Brasil. https://brasil.mapbiomas.org/downloads/mapas-para-download-geotiff/

Contact

For questions, suggestions, or research collaboration opportunities, please contact the authors:

• Ericson Gonçalves dos Santos: ericson.goncalves@ufv.br

Acknowledgements

• This work was supported by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES), Finance Code 001, and the Minas Gerais Research Support Foundation (FAPEMIG), through the R&D&I Agreement for Scholarship Quota Grant No. 5.12/2022. This work also received support from project No. 406426/2022-8, “Technologies for Adapting Buildings to Climate Change: Energy Efficiency, Photovoltaic Generation, and Emission Reduction in Schools,” funded by the National Council for Scientific and Technological Development (CNPq) under grant No. 152426/2025-6, CNPq Call No. 49/2024, and from the Ibero-American Programme of Science and Technology for Development (CYTED), through the TRAPECIO Network.

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  Reference publication

  When using this database, its raster products, building footprint layer, or derived lookup tables in research publications, simulations, or other scientific work, please cite the dataset using its Zenodo record and DOI:

  Gonçalves dos Santos, E., do Sacramento Candido, M. H., Silva, S. E. da ., Chermont, M. V. de C., Barbosa Figueiredo, F., Camille Caetano, I., Resende de Bortoli, G., Carvalho, L. D., Carneiro, C., Epiphanio da Rosa, M., Carlo, J. C., Stephan, I. I. C., Lucarelli, C. de C., & Menezes Oliveira, M. (2026). Integrated Geospatial Database of Land Cover, Building Morphology, Local Climate Zones, and ENVI-met Material Parameters for Viçosa, Brazil [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.23272020

  • Zenodo dataset: [10.5281/zenodo.23272020]