Offre en lien avec l’Action/le Réseau : SaD-2HN/– — –

Laboratoire/Entreprise : Laboratoire d’Informatique de Grenoble
Durée : 3
Contact : camille.bernard@univ-grenoble-alpes.fr
Date limite de publication : 2026-12-30

Contexte :
Human societies rely on geographic information to describe physical (e.g., schools, roads, cycle lanes) or virtual (e.g., administrative zoning) features that constitute their territories. In Geographic Information Science, the representation of such geographic entities is well established. However, representing and contextualizing the changes these entities undergo over time—driven, for example, by urban policies promoting low-carbon mobility—remains a major challenge.
These complex phenomena, also referred to as territorial transformations or spatial-temporal changes, involve one or several geographic entities that evolve at a given time or over a defined period under various pressures. Today, documenting these transformations in a machine-readable and computationally exploitable form is still largely absent.
In urban analysis, geographic databases describe—through standards such as CityGML or other formats—the built environment, transport networks, and urban infrastructure. Some open and collaborative databases such as OpenStreetMap (OSM) are often more up to date than official datasets due to continuous user contributions, thereby indirectly capturing urban evolution. Another more marginal initiative, OpenHistoricalMap (OHM), uses the OSM framework to build an open, editable historical map. However, none of these databases or standards explicitly model or document the changes undergone by geographic entities (e.g., transport networks, streets, cycle lanes, etc.).
Although some datasets, such as the IGN BD TOPO, provide differences between successive versions, these differences do not qualify or semantically describe the nature of the changes. Consequently, monitoring urban evolution remains difficult due to the lack of standards for representing territorial transformations. Yet, adopting a shared vocabulary for describing urban changes would make it possible to quantify them, define indicators of urban evolution, and facilitate comparisons between cities with similar or divergent trajectories.
In response to the lack of structured data describing territorial changes, and the absence of standardized tools for representing such transformations in urban planning and land-use monitoring, the objective of this PhD is to automate the detection and description of territorial changes, and to build temporal sequences of these changes in order to characterize territorial trajectories. These change catalogues will support interoperability between systems and provide a foundation for broader community adoption. They will take the form of Spatiotemporal Knowledge Graphs (ST-KGs), designed according to FAIR principles (Findable, Accessible, Interoperable, Reusable), ensuring accessibility and reuse, notably for digital twins of territories and predictive AI systems aimed at forecasting urban evolution based on historical data.

Sujet :
Geographic databases such as OpenStreetMap (OSM) and the IGN BD TOPO are continuously evolving to reflect territorial transformations. Identifying, describing, and characterizing the changes occurring in these geospatial databases over time is a major challenge for understanding and analyzing the evolution of territories.
This PhD is part of the ANR GEvoK project (Geographic Entities Evolution in Knowledge Graphs), which aims at the automatic detection and semantic annotation of geographic changes from heterogeneous data sources, including satellite imagery and collaborative datasets such as OpenStreetMap.
The proposed approach combines artificial intelligence techniques for the automatic detection of temporal changes, Semantic Web technologies for representing and enriching the knowledge derived from these detections, and large language models (LLMs) for querying the resulting knowledge bases and generating natural language narratives describing observed changes.

Profil du candidat :
The candidate must hold a Master’s degree in Computer Science, Geomatics, Data Science, or Artificial Intelligence.
•

Formation et compétences requises :

• Strong knowledge of Python and machine learning (classification, anomaly detection, clustering);
• Knowledge of Semantic Web technologies and knowledge representation (RDF, OWL, SPARQL);
• Basic knowledge of geographic data processing; familiarity with QGIS and spatial data formats (GeoJSON, shapefile, etc.);
• Autonomy, rigor, analytical skills, and interest in hybrid AI / geospatial approaches.
Required level of French: Upper intermediate (B2)
Required level of English: Upper intermediate (B2)

Adresse d’emploi :
LIG, Bâtiment IMAG, 700 Av. Centrale, 38401 Saint-Martin-d’Hères

Document attaché : 202607031244_offre-these-gevok-26-en.pdf