17 citations · 72 across the 26 of their papers we have counts for
30 papers
UNIGEOCLIP: Unified Geospatial Contrastive Learning
Guillaume Astruc, Eduard Trulls, Jan Hosang +2
The growing availability of co-located geospatial data spanning aerial imagery, street-level views, elevation models, text, and geographic coordinates offers a unique opportunity f…
Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Model
Marta López-Rauhut, Loic Landrieu, Mathieu Aubry +1
New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy is driving steady increases…
AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities
Guillaume Astruc, Nicolas Gonthier, Clement Mallet +1
Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurati…
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
Anatol Garioud, Nicolas Gonthier, Loic Landrieu +5
We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provide…
Efficient 3D Semantic Segmentation with Superpoint Transformer
Damien Robert, Hugo Raguet, Loic Landrieu
We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition p…
Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans
Romain Loiseau, Elliot Vincent, Mathieu Aubry +1
We propose an unsupervised method for parsing large 3D scans of real-world scenes with easily-interpretable shapes. This work aims to provide a practical tool for analyzing 3D scen…
A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds
Raphael Sulzer, Renaud Marlet, Bruno Vallet +1
We present a comprehensive survey and benchmark of both traditional and learning-based methods for surface reconstruction from point clouds. This task is particularly challenging f…
A Model You Can Hear: Audio Identification with Playable Prototypes
Romain Loiseau, Baptiste Bouvier, Yann Teytaut +3
Machine learning techniques have proved useful for classifying and analyzing audio content. However, recent methods typically rely on abstract and high-dimensional representations…
Online Segmentation of LiDAR Sequences: Dataset and Algorithm
Romain Loiseau, Mathieu Aubry, Loïc Landrieu
Roof-mounted spinning LiDAR sensors are widely used by autonomous vehicles. However, most semantic datasets and algorithms used for LiDAR sequence segmentation operate on $360^\cir…
Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans
Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet +1
The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across…
Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation
Damien Robert, Bruno Vallet, Loic Landrieu
Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network and projecting learned…
Deep Surface Reconstruction from Point Clouds with Visibility Information
Raphael Sulzer, Loic Landrieu, Alexandre Boulch +2
Most current neural networks for reconstructing surfaces from point clouds ignore sensor poses and only operate on raw point locations. Sensor visibility, however, holds meaningful…
Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning
Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet +1
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for…
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
Ekaterina Kalinicheva, Loic Landrieu, Clément Mallet +1
We propose a new deep learning-based method for estimating the occupancy of vegetation strata from 3D point clouds captured from an aerial platform. Our model predicts rasterized o…
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series
Vivien Sainte Fare Garnot, Loic Landrieu, Nesrine Chehata
Optical and radar satellite time series are synergetic: optical images contain rich spectral information, while C-band radar captures useful geometrical information and is immune t…
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
Félix Quinton, Loic Landrieu
While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of t…
Representing Shape Collections with Alignment-Aware Linear Models
Romain Loiseau, Tom Monnier, Mathieu Aubry +1
In this paper, we revisit the classical representation of 3D point clouds as linear shape models. Our key insight is to leverage deep learning to represent a collection of shapes a…
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
Vivien Sainte Fare Garnot, Loic Landrieu
Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise panoptic segmentation of ag…
Scalable Surface Reconstruction with Delaunay-Graph Neural Networks
Raphael Sulzer, Loic Landrieu, Renaud Marlet +1
We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds. Our approach can cope with the scale and variety of…
Torch-Points3D: A Modular Multi-Task Frameworkfor Reproducible Deep Learning on 3D Point Clouds
Thomas Chaton, Nicolas Chaulet, Sofiane Horache +1
We introduce Torch-Points3D, an open-source framework designed to facilitate the use of deep networks on3D data. Its modular design, efficient implementation, and user-friendly int…
Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
Vivien Sainte Fare Garnot, Loic Landrieu
The increasing accessibility and precision of Earth observation satellite data offers considerable opportunities for industrial and state actors alike. This calls however for effic…
Leveraging Class Hierarchies with Metric-Guided Prototype Learning
Vivien Sainte Fare Garnot, Loic Landrieu
In many classification tasks, the set of target classes can be organized into a hierarchy. This structure induces a semantic distance between classes, and can be summarised under t…
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano +1
Satellite image time series, bolstered by their growing availability, are at the forefront of an extensive effort towards automated Earth monitoring by international institutions.…
Supervized Segmentation with Graph-Structured Deep Metric Learning
Loic Landrieu, Mohamed Boussaha
We present a fully-supervized method for learning to segment data structured by an adjacency graph. We introduce the graph-structured contrastive loss, a loss function structured b…