Hi! I am a researcher at IGN, the French National Geographical Institute, in the computer science department STRUDEL. The focus of my research is on developing new optimization and learning methods to exploit the structure of remote sensing data (spectral, spatial, temporal, multi-modal) for improved precision and speed.

During my PhD I studied learning and optimization structured by large graphs (total variation, graphical models, etc). I now work on remote sensing applications such as  3D point clouds analysis
and satellite superspectral time-series. I have also developed an interest for structured deep models, such as graph convolutions and attention-based encoders.

See my academic_CV for more details (updated in Dec 2020).

NEW: Our CVPR Workshop EarthVision have been accepted. Great line-up of speakers, contests, and as always a must for meeting remote-sensing researchers intersted in the latest advances in computer vision. stay tuned for more info!

NEW: Our paper presenting Torch-points3D havs been accepted as an oral at 3DV 2020. Congrats to the awesome dev team for their impressive work.

NEW: Our newest paper on satellite time series with Vivien Sainte-Fare Garnot has been acccepted at CVPR 2020 for an oral! Check the paper and the code. Comes with a new benchmark too.

Selected Articles

Leveraging Class Hierarchies with Metric-Guided Prototype Learning
Vivien Sainte Fare Garnot, Loic Landrieu, 2020 preprint

Torch-Points3D: A Modular Multi-Task Framework for Reproducible Deep Learning on 3D Point Clouds, Thomas Chaton, Nicolas Chaulet, Sofiane Horache, Loic Landrieu, 3DV2020 (oral) arxiv

Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention, Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano, Nesrine Chehata, CVPR2020 (oral). [arXiv]

Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning, Loic Landrieu, Mohamed Boussaha, CVPR2019. [arXiv]

Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation, Hugo Raguet and Loic Landrieu, ICML2018  [arXiv]

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs, Loic landrieu and Martin Simonovsky, CVPR2018. [arXiv]

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

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] [isprs]

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


List of current and past PhD and Post Doc students:
Stéphane Guinard (Univ. Laval)
Mohamed Boussaha (Gambi-M)
Vivien Sainte-Fare Garnot
Raphael Sulzer
Romain Loiseau
Damien Robert
Ekaterina Kalinicheva ‎(post doc)

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.

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