Semantic Interoperability in Topographic Mapping: Qualitative Analysis of Land Use and Land Cover Classes Using Discursive Textual
Article Sidebar
Main Article Content
Abstract
The growing demand for geospatial data integration in Brazil intensifies the challenges of interoperability between cartographic sources, especially for Land Use and Land Cover (LULC) classes in national topographic mapping. This study investigates how conceptual and structural characteristics of the topographic model influence its compatibility with official thematic mappings, with an emphasis on IBGE products. Technical interviews were conducted with 12 experts, including analysts from public institutions responsible for mapping these categories and researchers in the field. The corpus was analysed using Discursive Textual Analysis (DTA), with support from IRaMuTeQ, combining discourse segmentation, lexicometric analyses, and interpretative synthesis by questionnaire dimensions. Based on the findings, a proposal was developed to adapt the conceptual model, represented in OMT-G, with a view to supporting the integration of topographic and thematic products at a scale of 1:25,000. The results indicate semantic divergences, classification gaps and limitations in the multiscale representation of environmental phenomena, with recommendations for adjustments in classes such as ‘Forest,’ ‘Campinarana,’ ‘Exposed Soil’ and ‘Savannah,’ as well as scale-dependent guidelines for humid environments and exposed features. As a contribution, the article systematises recommendations derived from institutional technical knowledge and proposes guidelines for conceptual restructuring aligned with classification clarity, semantic compatibility, and usability in digital contexts. As a follow-up, it is recommended to validate the proposal through pilot application and testing with users and institutions in data integration flows.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish in this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see "The Effect of Open Access").
How to Cite
References
Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, C. M. (2002). A review and assessment of land-use change models: Dynamics of space, time, and human choice. Gen. Tech. Rep. NE-297. Newton Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 61 p., 297. https://doi.org/10.2737/NE-GTR-297
Al-Yadumi, S., Xion, T. E., Wei, S. G. W., & Boursier, P. (2021). Review on Integrating Geospatial Big Datasets and Open Research Issues. IEEE Access, 9, 10604–10620. https://doi.org/10.1109/ACCESS.2021.3051084
Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use and land cover classification system for use with remote sensor data. Em Professional Paper (N. 964). U.S. Geological Survey. https://doi.org/10.3133/pp964
Araujo, V. S., Camboim, S.P., & Luz, N. B. (2025). Representação Cartográfica de Uso e Cobertura da Terra em Mapas Topográficos: Integração entre Experiência de Especialistas e Teorias da Percepção e Cognição. Zenodo. I Congresso Internacional de Regularização Fundiária & VIII Simpósio Brasileiro de Ciências Geodésicas e Tecnologias da Geoinformação (I CIRF & VIII SIMGEO), Recife, PE, Brazil. https://doi.org/10.5281/zenodo.17132576
Ballatore, A., Bertolotto, M., & Wilson, D. C. (2013). Geographic knowledge extraction and semantic similarity in OpenStreetMap. Knowledge and Information Systems, 37(1), 61–81. https://doi.org/10.1007/s10115-012-0571-0
Borges, K. A. V., Davis, C. A., & Laender, A. H. F. (2001). OMT-G: An Object-Oriented Data Model for Geographic Applications. GeoInformatica, 5(3), 221–260. https://doi.org/10.1023/A:1011482030093
Bravo, J. V. M. (2014). A confiabilidade semântica das informações geográficas voluntárias como função da organização mental do conhecimento espacial [Dissertação (mestrado), Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas.]. https://acervodigital.ufpr.br/handle/1884/37998
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A. M. (2022). Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data, 9(1), 251. https://doi.org/10.1038/s41597-022-01307-4
Buckley, A., Frye, C., & Buttenfield, B. (2005). An information model for maps: Towards cartographic production from GIS databases. 22nd ICA Conference Proceedings, A Coruña, Spain. https://www.academia.edu/download/47974279/An_information_model_for_maps_Towards_ca20160811-20869-xqmv8t.pdf
Camargo, B. V., & Justo, A. M. (2013). IRAMUTEQ: Um software gratuito para análisede dados textuais. Temas em Psicologia, 21(2), 513–518. https://doi.org/10.9788/TP2013.2-16
CONCAR (2017). Especificações técnicas para estruturação de dados geoespaciais vetoriais (ET-EDGV 3.0). Comissão Nacional de Cartografia.
CONCAR (2018). Norma da Especificação Técnica para Aquisição de Dados Geoespaciais Vetoriais. (ET-ADGV). Comissão Nacional de Cartografia.
Decreto-lei nº 243, de 28 de fevereiro de 1967 (1967). Fixa as Diretrizes e Bases da Cartografia Brasileira e dá outras providências. https://www.planalto.gov.br/ccivil_03/decreto-lei/1965-1988/Del0243.htm
Doan, A., & Halevy, A. Y. (2005). Semantic-Integration Research in the Database Community: A Brief Survey. AI Magazine, 26(1), 83–94. https://doi.org/10.1609/aimag.v26i1.1801
Dumont, M., Touya, G., & Duchêne, C. (2020). Designing multi-scale maps: Lessons learned from existing practices. International Journal of Cartography, 6(1), 121–151. https://doi.org/10.1080/23729333.2020.1717832
Flick, U. (2008). Introdução à Pesquisa Qualitativa—3.ed. Artmed Editora.
Gunst, M., Van Oosterom, P., & Berry, V. A. N. (2000). MODELING, COMPUTING AND CLASSIFYING TOPOGRAPHIC AREA FEATURES BASED ON TOPOLOGICALLY NON-STRUCTURED LINE INPUT DATA. International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Supplement B4. ISPRS2000. https://www.isprs.org/proceedings/XXXIII/congress/part4/43_XXXIII-part4s.pdf
IBGE (2012). Manual Técnico da Vegetação Brasileira (2º ed.). Instituto Brasileiro de Geografia e Estatística.
IBGE (2013). Manual Técnico de Uso da Terra (3º ed.). Instituto Brasileiro de Geografia e Estatística.
Kokla, M., & Guilbert, E. (2020). A Review of Geospatial Semantic Information Modeling and Elicitation Approaches. ISPRS International Journal of Geo-Information, 9(3). https://doi.org/10.3390/ijgi9030146
Kronenfeld, B. J., Buttenfield, B. P., Stanislawski, L. V., Kronenfeld, B. J., Buttenfield, B. P., & Stanislawski, L. V. (2020). Map Generalization for the Future: Editorial Comments on the Special Issue. ISPRS International Journal of Geo-Information, 9(8). https://doi.org/10.3390/ijgi9080468
Lutz, M., Riedemann, C., & Probst, F. (2003). A Classification Framework for Approaches to Achieving Semantic Interoperability between GI Web Services. Em W. Kuhn, M. F. Worboys, & S. Timpf (Org.), Spatial Information Theory. Foundations of Geographic Information Science (V. 2825, p. 186–203). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-39923-0_13
Machado, A. A., & Camboim, S. P. (2024). Semantic Alignment of Official and Collaborative Geospatial Data: A Case Study in Brazil. Revista Brasileira de Cartografia, 76. https://doi.org/10.14393/rbcv76n0a-72070
Marcelino, V., & Silva, A. R. da. (2025). ANÁLISE TEXTUAL DISCURSIVA DE DOCUMENTOS: UM PERCURSO PARA NOVAS COMPREENSÕES. Vestigare: Revista de Pesquisas em Educação, Ciências e Tecnologias, 1, 143–159. https://doi.org/10.5380/vrpect.1.98026
Martins, K. N., Paula, M. C. de, Gomes, L. P. S., & Santos, J. E. dos. (2022). O software IRaMuTeQ como recurso para a análise textual discursiva. Revista Pesquisa Qualitativa, 10(24), 213–232. https://doi.org/10.33361/RPQ.2022.v.10.n.24.383
McMaster, R. B., & Shea, K. S. (1992). Generalization in digital cartography. http://geoinformatics.ntua.gr/courses/admcarto/lecture_notes/generalisation/bibliography/mcmaster_shea_1992.pdf
Olszewski, R., Iwaniak, A., & Gotlib, D. (2005). SDI in Poland–concept of topographic reference system for thematic, harmonized databases. ICA Conference, La Coruna. https://icaci.org/files/documents/ICC_proceedings/ICC2005/htm/pdf/poster/TEMA6/R.%20OLSZEWSKI.pdf
Petkov, D., & Bandrova, T. (2020). Classification of cartographic models according to their content, dimensionality, material of production and types of reality. ИнтерКарто. ИнтерГИС, 26(1), 434–446.
Real, F. Q., Bella, G., McNeill, F., & Bundy, A. (2020). Using domain lexicon and grammar for ontology matching. The Fifteenth International Workshop on Ontology Matching, 1–12. https://www.research.ed.ac.uk/en/publications/using-domain-lexicon-and-grammar-for-ontology-matching
Reitsma, F., & Bittner, T. (2003). Scale in Object and Process Ontologies. Em W. Kuhn, M. F. Worboys, & S. Timpf (Org.), Spatial Information Theory. Foundations of Geographic Information Science (V. 2825, p. 13–27). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-39923-0_2
Robinson, A. C., Demšar, U., Moore, A. B., Buckley, A., Jiang, B., Field, K., Kraak, M.-J., Camboim, S. P., & Sluter, C. R. (2017). Geospatial big data and cartography: Research challenges and opportunities for making maps that matter. International Journal of Cartography, 3(sup1), 32–60. https://doi.org/10.1080/23729333.2016.1278151
Rocha Salamanca, L. A., & Caro Arias, C. I. (1995). Data Model of the Geographic Institute Agustin Codazzi “SIGAC”. In Proceedings of the 17th International Cartographic Conference (ICC) (pp. 85–93). International Cartographic Association. https://icaci.org/files/documents/ICC_proceedings/ICC1995/PDF/Cap019.pdf
Silva, L. S. L., & Camboim, S. P. (2020). BRAZILIAN NSDI TEN YEARS LATER: CURRENT OVERVIEW, NEW CHALLENGES AND PROPOSITIONS FOR NATIONAL TOPOGRAPHIC MAPPING. Boletim de Ciências Geodésicas, 26, e2020018. https://doi.org/10.1590/s1982-21702020000400018
Sluter, C. R., Camboim, S. P., Iescheck, A. L., Pereira, L. B., Castro, M. C., Yamada, M. M., & Araújo, V. S. (2018). A Proposal for Topographic Map Symbols for Large-Scale Maps of Urban Areas in Brazil. The Cartographic Journal, 55(4), 362–377. https://doi.org/10.1080/00087041.2018.1549307
Souza, F. A., Silva, E. D. B. da, & Camboim, S. P. (2025). Explorando o Uso de Large Language Model (ChatGPT) para Alinhamento Semântico entre Esquemas Conceituais de Dados Geoespaciais. Revista Brasileira de Cartografia, 77. https://doi.org/10.14393/rbcv77n0a-75193
Steiniger, S., & Weibel, R. (2007). Relations among Map Objects in Cartographic Generalization. Cartography and Geographic Information Science, 34(3), 175–197. https://doi.org/10.1559/152304007781697866
Troumpoukis, A., Konstantopoulos, S., & Prokopaki-Kostopoulou, N. (2022). A geospatial source selector for federated GeoSPARQL querying. Open Research Europe, 2, 48. https://pmc.ncbi.nlm.nih.gov/articles/PMC10446020/
Yu, L., Qiu, P., Liu, X., Lu, F., & Wan, B. (2018). A holistic approach to aligning geospatial data with multidimensional similarity measuring. International Journal of Digital Earth, 11(8), 845–862. https://doi.org/10.1080/17538947.2017.1359688