3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
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…
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.…
Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series
Vivien Sainte Fare Garnot, Loic Landrieu, Sebastien Giordano +1
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respecti…