PhD Position F/M Spatio-temporal analysis of remote sensing data at large scales

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part.

With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation.

Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services.

It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more.

The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

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Context

In a world facing profound upheavals—ecological, energy-related, economic, health-related, social, and more—territories are at the heart of the most complex decisions. The stakeholders within these territories are under increasing pressure to anticipate, adapt, and invent new approaches to planning and development. It is now essential to be able to anticipate territorial evolution and simulate different management scenarios in order to assess, and even compare, their impacts. This is the objective of the Digital Twin of France and its Territories (JUNN) project, initiated and co-led by the IGN (National Institute of Geographic and Forest Information), Cerema (Center for Studies and Expertise on Risks, Environment, Mobility and Urban Planning), and Inria (National Institute for Research in Digital Science and Technology).

The JUNN project will leverage an unprecedented amount of remote sensing data on the entire French territory to process and analyze. In particular, the airborne LidarHD campaigns and satellite-based Digital Surface Models will provide spatio-temporal 3D data that will be used, not only for updating 3D city models over time, but also for better understanding the evolution of urban landscapes during long term periods. In this context, designing methods for efficiently analyzing the geometric changes and understanding the evolution of urban attributes such as urban growth and architectural variations constitutes a key scientific challenge.

Objectives

The goal of this PhD is to (i) develop efficient methods for detecting 3D changes and expressing them with simple geometric shapes, and (ii) analyze the spatio-temporal distribution of these geometric changes at large scales (i.e. from city districts to the entire country).

In contrast to existing 3D change detection methods that mostly operate from 3D point clouds, e.g. [1,2], the PhD candidate will investigate, as first objective, change detection methods that directly operate from more concise geometric primitives such as planes and 3D polygons. This strategic choice is motivated by both efficiency reasons as point-based methods suffer from a low scalability and interoperability reasons as such geometric primitives will directly feed the building reconstruction methods of the JUNN project for efficient 3D model updates. The candidate will investigate methods for detecting planar variations in a pair of point clouds. One possible solution will be to adapt static mechanisms such as [3] by using similarity metrics between planar shapes, as proposed in [4] for 3D data registration. The candidate will also investigate data structures to efficiently organize and parse the detected planar changes, e.g. by using Level of Detail trees [5].

The second objective will be to evaluate the potential of these detected spatio-temporal variations for understanding evolution of urban attributes. In particular, the PhD candidate will develop models for analyzing the spatio-temporal distribution of planar shapes at large scales and seek potential correlations on a variety of attributes that characterizes the city evolution in terms of shape, physics or functionality. A first naïve approach will be to extend the statistical models developed in [6] for basic 2D building footprints with more expressive 3D planar primitives.

Keywords

Geometry processing, 3D computer vision, machine learning, statistical analysis, urban reconstruction, planar shape detection

References

[1] Stilla and Xu. Change detection of urban objects using 3D point clouds: A review. P&RS journal, 2023 

[2] de Gélis, Lefèvre and Corpetti. 3D urban changes detection with point cloud siamese networks. ISPRS archives 2021

[3] Yu and Lafarge. Finding Good Configurations of Planar Primitives in Unorganized Point Clouds. CVPR 2022

[4] Li and Lafarge. Planar Shape Based Registration for Multi-modal Geometry. BMVC 2021

[5] Pan, Zhang, Liu, Gong and Huang. Building LOD Representation for 3D Urban Scenes. P&RS journal, 2025

[6] Zhu et al. GlobalBuildingAtlas: An Open Global and Complete Dataset of Building Polygons, Heights and LoD1 3D Models. ArXiv 2025.

More info on the position can be found at https://team.inria.fr/titane/files/2026/03/sujet_JNFT_spatiotemporal_analysis.pdf and on the JUNN project at https://team.inria.fr/titane/the-jnft-project-2026-2030/

The ideal candidate should have a strong background in 3D geometry, computer vision and machine learning, be able to program in C/C++ and Python, be fluent in English, and be creative and rigorous.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Contribution to mutual insurance (subject to conditions)

Gross Salary per month: 2300 €