PhD Position in Spatio-Temporal Analysis of Remote Sensing Data at Large Scales
Develop scalable methods for detecting and analyzing 3D urban changes using remote sensing data for national-scale urban evolution studies.
Best for:Best for computer science PhD candidates with expertise in 3D geometry processing, machine learning, and programming in C/C++/Python, seeking to contribute to large-scale urban data analysis.
Overview
This PhD position at Inria’s Côte d'Azur University Center focuses on advancing spatio-temporal analysis of remote sensing data to understand urban evolution at large scales. The project, part of the Digital Twin of France and its Territories (JUNN), aims to design efficient methods for detecting 3D geometric changes in urban landscapes using airborne LidarHD and satellite data. The candidate will develop algorithms for detecting planar variations in point clouds, analyze spatio-temporal distributions of urban attributes, and correlate these changes with city evolution metrics. The work bridges computer vision, machine learning, and urban reconstruction, with applications in sustainable urban planning and infrastructure management.
What you'll do
- Develop efficient algorithms for detecting 3D geometric changes in urban landscapes using airborne LidarHD and satellite data.
- Design methods for expressing detected changes using simple geometric primitives (planes, 3D polygons) to improve scalability and interoperability.
- Analyze spatio-temporal distributions of urban attributes (e.g., urban growth, architectural variations) at large scales (city districts to national level).
- Investigate data structures (e.g., Level of Detail trees) for organizing and parsing detected planar changes.
- Extend statistical models for analyzing 3D planar primitives to understand correlations with city evolution metrics.
- Collaborate with the JUNN project team to integrate findings into the Digital Twin of France framework.
- Publish research findings in top-tier conferences and journals within the field of computer vision and geospatial data science.
Requirements
Degree required: Master's degree (Bac+5) or equivalent in Computer Science, Applied Mathematics, or a related field
Degree offered: PhD
Language: English; fluency required for research and communication
Instruction: English
- Strong background in 3D geometry processing and computer vision
- Proficiency in programming with C/C++ and Python
- Experience with machine learning techniques for spatial data analysis
- Fluency in English (written and spoken)
- Ability to work with large-scale datasets and distributed computing frameworks
- No specific nationality or residency restrictions mentioned
- Open to all applicants with the required academic qualifications
Funding & terms
How to apply
Deadline: 2026-11-30
Applications must be submitted online through the Inria website. Ensure all required documents are uploaded directly via the portal. Applications sent through other channels are not guaranteed to be processed.
- CV
- Cover letter
- Academic transcripts (Bachelor’s and Master’s degrees)
- Proof of English proficiency (if applicable)
- Research statement or project proposal (optional but recommended)
Contact
Florent Lafarge· PhD Supervisor
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