I am a machine learning researcher at Imagine-lab, ENPC, and associate researcher at IGN (The French Mapping Agency) in the LASTIG lab. I am interested in geospatial machine learning and work with 3D point clouds and satellite image time-series.
Editorial Advisory Board of ISPRS Journal (Elsevier, IF:11.8) & Reviewing Committee of Remote Sensing (MDPI, IF:5.3).
Co-chair of the ISPRS working group Temporal Geospatial Data Understanding
Co-lead of the IEEE GRSS working group Image and Signal Processing
Co-organizer of the EarthVision CVPR Workshop (2021,2022,2023)
Co-Program chair of the XXIVth ISPRS congress
Principal investigator of the ReADy3D ANR project on Dynamic 3D analysis for autonomous driving.

See my academic_CV for more details (updated in June 2023).

PhD offer with EFEO on machine learning for digital archeology.

🆕 I defended my Habilitation à Diriger la Recherche (HDR)!
🆕 Just started at Imagine-lab, ENPC.
🆕 Our Workshop EarthVision has been renewed at CVPR2023. A great line-up of speakers, contests, and a must-attend event for researchers interested in remote sensing and computer vision.
🆕 My twins were born in October! 🍼🍼
🆕 Outstanding reviewer for ECCV22 and ISPRS-Congress 22.
🆕 Our paper on interpretable sound discovery with Romain Loiseau has been accepted at ISMIR2022.
🆕 Our paper on Online LiDAR segmentation with Romain Loiseau et Mathieu Aubry has been accepted at ECCV2022.
🆕 Our paper Deep View Aggregation with Damien Robert has been accepted at CVPR2022 for an oral 🎤, and was selected as a best paper finalist (top 33/2104).
🆕 3 workshop papers at CVPR2022 with my Romain Loiseau and Ekaterina Kalinicheva : Transformers for Vision, Sight and Sound, and EarthVision.
🆕 Our paper on surface reconstruction with Raphael Sulzer, Renaud Marlet, Alexandre Boulch and Bruno Vallet is accepted at ICPR2022.
🆕 Our paper on multimodal SITS analysis with Vivien Sainte-Fare Garnot has been published in IJPRS.
🆕 Outstanding reviewer for ICML21 and CVPR21.
🆕 Our paper on unsupervised and visual representation of large shape collections with Romain Loiseau, Tom Monnier, and Mathieu Aubry has been accepted at 3DV2021.
🆕 Our paper on panoptic segmentation of satellite time series, with Vivien Sainte-Fare Garnot has been accepted at ICCV2021. Check out PASTIS: the first SITS panoptic-ready dataset.

Selected Articles

A Model You Can Hear: Audio Identification with Playable Prototypes, Romain Loiseau, Baptiste Bouvier, Yann Teytaut, Elliot Vincent, Mathieu Aubry, Loic Landrieu ISMIR 2022
[project] [arXiv] [github]

Online Segmentation of LiDAR Sequences: Dataset and Algorithm, Romain Loiseau, Mathieu Aubry, Loic Landrieu, ECCV 2022
[arXiv][github] [data] [project]

Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation, Damien Robert, Bruno Vallet, Loic Landrieu CVPR 2022 (oral 🎤, best paper finalist 🏅)
[arXiv] [github]

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans, Ekaterina Kalinicheva, Loic Landrieu, Clement Mallet, Nesrine Chehata, CVPR Workshop EarthVision (oral 🎤) 2022
[arXiv] [github]

Deep Surface Reconstruction from Point Clouds with Visibility Information. Raphael Sulzer, Loic Landrieu, Alexandre Boulch, Renaud Marlet, Bruno Vallet, ICPR 2022
[arXiv] [github]

Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series, Vivien Sainte Fare Garnot, Loic Landrieu, ISPRS Journal of Photogrammetry and Remote Sensing 2022
 [arXiv] [data] [github]

Representing Shape Collections with Alignment-Aware Linear Models, Romain Loiseau, Tom Monnier, Mathieu Aubry, Loic Landrieu 3DV 2021
[arXiv] [project] [github]

Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks, Vivien Sainte Fare Garnot Loic Landrieu, ICCV 2021
[arXiv] [github] [data]

Scalable Surface Reconstruction with Delaunay-Graph Neural Networks, Raphael Sulzer, Loic Landrieu, Renaud Marlet, and Bruno Vallet, Eurographics SGP 2021
[arXiv] [github]

Leveraging Class Hierarchies with Metric-Guided Prototype Learning, Vivien Sainte Fare Garnot, Loic Landrieu, BMVC 2021
[arXiv] [github]

Torch-Points3D: A Modular Multi-Task Framework for Reproducible Deep Learning on 3D Point Clouds, Thomas Chaton, Nicolas Chaulet, Sofiane Horache, Loic Landrieu, 3DV 2020 (oral 🎤)
[arXiv] [github]

Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention, Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano, Nesrine Chehata, CVPR 2020 (oral 🎤).
[arXiv] [github]

Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning, Loic Landrieu, Mohamed Boussaha, CVPR 2019.
[arXiv] [github]

Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation, Hugo Raguet and Loic Landrieu, ICML 2018 
[arXiv] [github]

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs, Loic landrieu and Martin Simonovsky, CVPR 2018.
[arXiv] [github]

Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions on General Weighted Graphs. Loic Landrieu, Guillaume Obozinski, SIAM Journal on Imaging Science, 2017

A Structured Regularization Framework for Spatially Smoothing Semantic Labelings of 3D Point Clouds. Loic Landrieu, Hugo Raguet , Bruno Vallet , Clément Mallet, Martin Weinmann, ISPRS Journal of Photogrammetry and Remote Sensing, 2017
[hal] [github]

Preconditioning of a Generalized Forward-Backward Splitting and Application to Optimization on Graphs. Hugo Raguet, Loic Landrieu, SIAM Journal on Imaging Science, 2015 [arxiv] [github]


List of current and past PhD/Post Doc students:
Stéphane Guinard (now at Univ. Laval)
Mohamed Boussaha (now at Gambi-M)
Vivien Sainte-Fare Garnot (now at Univ. of Zurich)
Raphael Sulzer (now at INRIA Titane)
Ekaterina Kalinicheva ‎(post doc, now at CESBIO)
Romain Loiseau

Damien Robert

List of current and past interns:
Stephane Guinard, Simon Bailly, Omar Lahbib, Joana Roussillon, Thomas Luo (Helix.Re), Anna Kondracka (Vermessung AVT), Lamiae El-Mendili, Ameur Zaibi, Julien Baconat, Cédric Baron, Félix Quinton, Yaping Lin, Dina El-Zein, Guillaume Astruc, Hongyu Zhou.

I teach machine learning at master level at ENSG and ENPC.

I am a technical advisor for SAMP, a startup using machine learning to produce digital twins of industrial facilities.

I was selected as oustanding reviewer for ICML21, CVPR21, ECCV22, and ISPRS-Congress22.