{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T12:58:08Z","timestamp":1773838688328,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T00:00:00Z","timestamp":1773705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ANR and FNS","award":["TRACES (ANR-21-CE23-0034)"],"award-info":[{"award-number":["TRACES (ANR-21-CE23-0034)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the European Union\u2019s Open Data program promote spatiotemporal datasets (e.g., population, land use), analyzing and interpreting these data over time remains complex and requires technical expertise, limiting their accessibility. This research proposes Semantic Web-based methods to detect and annotate trends in spatiotemporal series, thereby assisting in the systematic analysis of temporal patterns. We introduce the SETT ontology (SEmantic Trajectory of Territory) and its OFF-SETT framework (Ontological Framework For SETT), enabling the formal description of territorial trends and their publication as semantic trajectories in the Linked Open Data cloud. The study delivers (i) a generic methodology for detecting and describing trajectories in spatiotemporal datasets; (ii) a framework for automatically generating knowledge graphs capturing these trajectories; (iii) a knowledge graph describing trajectories of demographic and satellite-derived variables (e.g., temperature, water, vegetation) for study areas in France and Switzerland; and (iv) a web-based geovisualization platform. The approach shows that Semantic Web technologies bridge complex spatiotemporal analysis and public accessibility. By publishing territorial trajectories as knowledge graphs, it fosters transparency, interoperability, and reuse of data, supporting informed decision-making and citizen engagement.<\/jats:p>","DOI":"10.3390\/ijgi15030132","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T14:50:48Z","timestamp":1773759048000},"page":"132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["OFF-SETT: A Semantic Framework for Annotating Trends in Spatiotemporal Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2246-6568","authenticated-orcid":false,"given":"Camille","family":"Bernard","sequence":"first","affiliation":[{"name":"University Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1398-7118","authenticated-orcid":false,"given":"J\u00e9r\u00f4me","family":"Gensel","sequence":"additional","affiliation":[{"name":"University Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France"}]},{"given":"Daniela F.","family":"Milon-Flores","sequence":"additional","affiliation":[{"name":"University Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France"}]},{"given":"Gregory","family":"Giuliani","sequence":"additional","affiliation":[{"name":"Institute for Environmental Sciences, University of Geneva, 1205 Geneva, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7242-6102","authenticated-orcid":false,"given":"Marl\u00e8ne","family":"Villanova","sequence":"additional","affiliation":[{"name":"University Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2023). Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Intergovernmental Panel on Climate Change. Technical Report."},{"key":"ref_2","unstructured":"IPCC (2022). Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Intergovernmental Panel on Climate Change. Technical Report."},{"key":"#cr-split#-ref_3.1","unstructured":"European Parliament and Council (2019). Directive"},{"key":"#cr-split#-ref_3.2","unstructured":"(EU) 2019\/1024 on Open Data and the Re-Use of Public Sector Information, Official Journal of the European Union."},{"key":"ref_4","unstructured":"European Parliament and Council (2007). Directive 2007\/2\/EC Establishing an Infrastructure for Spatial Information in the European Community (INSPIRE), Official Journal of the European Union."},{"key":"ref_5","first-page":"4","article-title":"Open data: An international comparison of strategies","volume":"12","author":"Huijboom","year":"2011","journal-title":"Eur. J. ePractice"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e56","DOI":"10.1017\/dap.2024.63","article-title":"Exploring the contributions of open data intermediaries for a sustainable open data ecosystem","volume":"6","author":"Shaharudin","year":"2024","journal-title":"Data Policy"},{"key":"ref_7","unstructured":"Eurostat (2024). Regional and Urban Statistics, European Commission."},{"key":"ref_8","unstructured":"European Environment Agency (2024). Copernicus Land Monitoring Service. Land Cover and Land Use Data, European Environment Agency."},{"key":"ref_9","unstructured":"Parsons, A., and Powell-Smith, A. (2026, March 11). Unlocking the Value of Fragmented Public Data. mySociety. Available online: https:\/\/research.mysociety.org\/html\/unlocking-fragmented-data\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.3390\/ijgi4042306","article-title":"Spatiotemporal Data Mining: A Computational Perspective","volume":"4","author":"Shekhar","year":"2015","journal-title":"ISPRS Int. J.-Geo-Inf."},{"key":"ref_11","first-page":"83","article-title":"Spatio-temporal data mining: A survey of problems and methods","volume":"51","author":"Atluri","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1145\/2379776.2379788","article-title":"Time-Series Data Mining","volume":"45","author":"Esling","year":"2012","journal-title":"ACM Comput. Surv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Militino, A.F., Ugarte, M.D., and P\u00e9rez-Goya, U. (2018). An Introduction to the Spatio-Temporal Analysis of Satellite Remote Sensing Data for Geostatisticians. Geostatistics Valencia 2016, Springer.","DOI":"10.1007\/978-3-319-78999-6_13"},{"key":"ref_14","first-page":"601","article-title":"Temporal trend evaluation in monitoring programs with high spatial resolution and low temporal resolution using geographically weighted regression models","volume":"195","author":"Norling","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_15","unstructured":"Habereder, I., Kneib, T., Echizen, I., and Spinde, T. (2025). A Systematic Review of Spatio-Temporal Statistical Models: Theory, Structure, and Applications. arXiv."},{"key":"ref_16","first-page":"28","article-title":"The Semantic Web","volume":"284","author":"Hendler","year":"2001","journal-title":"Sci. Am."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MIS.2006.62","article-title":"The Semantic Web Revisited","volume":"21","author":"Shadbolt","year":"2006","journal-title":"IEEE Intell. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Heath, T., and Bizer, C. (2011). Linked Data: Evolving the Web into a Global Data Space, Springer. Synthesis Lectures on Data, Semantics, and Knowledge (SLDSK) Series.","DOI":"10.1007\/978-3-031-79432-2"},{"key":"ref_19","first-page":"11","article-title":"Towards semantic enrichment of Earth Observation data: The LEODS framework","volume":"5","author":"Bernard","year":"2024","journal-title":"AGILE Gisci. Ser."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/978-3-031-60796-7_5","article-title":"Publication of Satellite Earth Observations in the Linked Open Data Cloud: Experiment Through the TRACES Project","volume":"Volume 14673","author":"Bernard","year":"2024","journal-title":"Proceedings of the Web and Wireless Geographical Information Systems"},{"key":"ref_21","unstructured":"Milon-Flores, D.F., Bernard, C., Gensel, J., and Giuliani, G. (2023). Detection and semantic description of changes in Earth Observation Time Series data. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer."},{"key":"ref_22","first-page":"169","article-title":"The Semantic Web: Two decades on","volume":"11","author":"Hogan","year":"2020","journal-title":"Semant. Web"},{"key":"ref_23","unstructured":"Richard Cyganiak, D.R., and Tennison, J. (2026, March 11). The RDF Data Cube Vocabulary. Available online: https:\/\/www.w3.org\/TR\/vocab-data-cube\/."},{"key":"ref_24","unstructured":"Hobbs, J.R., and Pan, F. (2024, July 12). OWL Time Ontology. Available online: https:\/\/www.w3.org\/TR\/owl-time\/."},{"key":"ref_25","unstructured":"Open Geospatial Consortium (2024, July 12). OGC GeoSPARQL\u2014A Geographic Query Language for RDF Data. Available online: https:\/\/www.ogc.org\/standards\/geosparql."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fensel, D., \u015eim\u015fek, U., Angele, K., Huaman, E., K\u00e4rle, E., Panasiuk, O., Toma, I., Umbrich, J., and Wahler, A. (2020). Introduction: What Is a Knowledge Graph?. Knowledge Graphs: Methodology, Tools and Selected Use Cases, Springer International Publishing.","DOI":"10.1007\/978-3-030-37439-6"},{"key":"ref_27","first-page":"71","article-title":"Knowledge graphs","volume":"54","author":"Hogan","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Th\u00e9riault, M., Seguin, A.M., Aub\u00e9, Y., and Villeneuve, P.Y. (1999). A spatio-temporal data model for analysing personal biographies. Proceedings of Tenth International Workshop on Database and Expert Systems Applications, DEXA 99, Florence, Italy, 1\u20133 September 1999, IEEE.","DOI":"10.1109\/DEXA.1999.795202"},{"key":"ref_29","unstructured":"Mannhardt, F., and Blinde, D. Analyzing the trajectories of patients with sepsis using process mining. Proceedings of the RADAR + EMISA 2017, Available online: https:\/\/ceur-ws.org\/Vol-1859\/bpmds-08-paper.pdf."},{"key":"ref_30","first-page":"23","article-title":"Towards Semantic Trajectory Data Analysis: A Conceptual and Computational Approach","volume":"Volume 3","author":"Yan","year":"2009","journal-title":"VLDB PhD Workshop"},{"key":"ref_31","unstructured":"Noel, D., Villanova-Oliver, M., Gensel, J., and Le Qu\u00e9au, P. (2017). Design patterns for modelling life trajectories in the semantic web. Proceedings of the Web and Wireless Geographical Information Systems: 15th International Symposium, W2GIS 2017, Shanghai, China, 8\u20139 May 2017, Proceedings 15, Springer."},{"key":"ref_32","unstructured":"Camara, G. (2022). On the semantics of big Earth observation data for land classification. arXiv."},{"key":"ref_33","unstructured":"Milon Flores, D. (2025). OFf-SETT: Un Framework Ontologique Pour les Trajectoires S\u00e9mantiques Environnementales des Territoires. [Ph.D. Thesis, Universit\u00e9 Grenoble Alpes]."},{"key":"ref_34","unstructured":"Noy, N.F., and McGuinness, D.L. (2026, March 11). Ontology Development 101: A Guide to Creating Your First Ontology. Available online: https:\/\/corais.org\/sites\/default\/files\/ontology_development_101_aguide_to_creating_your_first_ontology.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111181","DOI":"10.1016\/j.rse.2019.04.034","article-title":"Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm","volume":"232","author":"Zhao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107299","DOI":"10.1016\/j.sigpro.2019.107299","article-title":"Selective review of offline change point detection methods","volume":"167","author":"Truong","year":"2020","journal-title":"Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1007\/s10618-023-00923-x","article-title":"ClaSP: Parameter-free time series segmentation","volume":"37","author":"Ermshaus","year":"2023","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Awty-Carroll, K., Bunting, P., Hardy, A., and Bell, G. (2019). An evaluation and comparison of four dense time series change detection methods using simulated data. Remote Sens., 11.","DOI":"10.3390\/rs11232779"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113222","DOI":"10.1016\/j.rse.2022.113222","article-title":"Trend, seasonality, and abrupt change detection method for land surface temperature time-series analysis: Evaluation and improvement","volume":"280","author":"Li","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Loew, A., Holmes, T., and de Jeu, R. (2009). The European heat wave 2003: Early indicators from multisensoral microwave remote sensing?. J. Geophys. Res. Atmos., 114.","DOI":"10.1029\/2008JD010533"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/j.1467-8306.1963.tb00429.x","article-title":"Generalization in statistical mapping","volume":"53","author":"Jenks","year":"1963","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_43","first-page":"35","article-title":"A method for implementing a statistically significant number of data classes in the Jenks algorithm","volume":"Volume 1","author":"North","year":"2009","journal-title":"Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, China, 14\u201316 August 2009"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/3\/132\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T09:32:02Z","timestamp":1773826322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/15\/3\/132"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["ijgi15030132"],"URL":"https:\/\/doi.org\/10.3390\/ijgi15030132","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,17]]}}}