Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Mallet, Clement"'
Publikováno v:
ECCV 2024
The diversity and complementarity of sensors available for Earth Observations (EO) calls for developing bespoke self-supervised multimodal learning approaches. However, current multimodal EO datasets and models typically focus on a single data type,
Externí odkaz:
http://arxiv.org/abs/2404.08351
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 (RS) semantic segmentation is costly and time-consuming. Unsupervised Do
Externí odkaz:
http://arxiv.org/abs/2304.07750
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 initial topology of the given input, which is detrimental for numerous dow
Externí odkaz:
http://arxiv.org/abs/2207.11446
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 all vegetation layers, most datasets and methods focus only on the se
Externí odkaz:
http://arxiv.org/abs/2204.11620
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 three vegetation strata corresponding to lower, medium, and higher cov
Externí odkaz:
http://arxiv.org/abs/2201.08051
Publikováno v:
SilviLaser 2021 Conference
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 occupancy maps for three vegetation strata: lower, medium, and higher s
Externí odkaz:
http://arxiv.org/abs/2112.13583
Autor:
Chazalon, Joseph, Carlinet, Edwin, Chen, Yizi, Perret, Julien, Duménieu, Bertrand, Mallet, Clément, Géraud, Thierry, Nguyen, Vincent, Nguyen, Nam, Baloun, Josef, Lenc, Ladislav, Král, Pavel
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, drawn at 1/5000 scale between 1894 and 1937. The competition feature
Externí odkaz:
http://arxiv.org/abs/2105.13265
Autor:
Chen, Yizi, Carlinet, Edwin, Chazalon, Joseph, Mallet, Clément, Duménieu, Bertrand, Perret, Julien
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 the time for subsequent thematic analysis. The goal of this work is t
Externí odkaz:
http://arxiv.org/abs/2101.02144
Publikováno v:
In Pattern Recognition November 2023 143
Akademický článek
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