1 citations · 4 across the 7 of their papers we have counts for
8 papers
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
Emilie Vautier, Clément Mallet, Cédric Vega
Forest attributes are essential for national-scale resource monitoring. Airborne LiDAR metrics are among the auxiliary variables most strongly correlated with forest attributes use…
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Valerio Marsocci, Nicolas Gonthier, Anatol Garioud +2
Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (R…
BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation
Minh On Vu Ngoc, Yizi Chen, Nicolas Boutry +2
Capturing the global topology of an image is essential for proposing an accurate segmentation of its domain. However, most of existing segmentation methods do not preserve the init…
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…
ICDAR 2021 Competition on Historical Map Segmentation
Joseph Chazalon, Edwin Carlinet, Yizi Chen +9
This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France…
Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation
Yizi Chen, Edwin Carlinet, Joseph Chazalon +3
The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through…