Zobrazeno 1 - 10
of 380
pro vyhledávání: '"LEFÈVRE, Sébastien"'
Autor:
Uzun, Baki, Pande, Shivam, Cachin-Bernard, Gwendal, Pham, Minh-Tan, Lefèvre, Sébastien, Blatrix, Rumais, McKey, Doyle
Regular patterns of vegetation are considered widespread landscapes, although their global extent has never been estimated. Among them, spotted landscapes are of particular interest in the context of climate change. Indeed, regularly spaced vegetatio
Externí odkaz:
http://arxiv.org/abs/2409.00518
Autor:
Pande, Shivam, Uzun, Baki, Guiotte, Florent, Corpetti, Thomas, Delerue, Florian, Lefèvre, Sébastien
In this study, we tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution, meticul
Externí odkaz:
http://arxiv.org/abs/2409.00513
Autor:
Belmouhcine, Abdelbadie, Burnel, Jean-Christophe, Courtrai, Luc, Pham, Minh-Tan, Lefèvre, Sébastien
Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area focus on the use of generic object detection and do not leverage the
Externí odkaz:
http://arxiv.org/abs/2307.06724
This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. Such an approach leverages an anomaly detection framework that computes metrics directly on the input space, enhancin
Externí odkaz:
http://arxiv.org/abs/2307.06720
Autor:
Heidler, Konrad, Mou, Lichao, Loebel, Erik, Scheinert, Mirko, Lefèvre, Sébastien, Zhu, Xiao Xiang
Choosing how to encode a real-world problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often been approached as a semantic segmentation task. Recent studies have show
Externí odkaz:
http://arxiv.org/abs/2307.03461
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing Volume 206, December 2023, Pages 168-183
In a constant evolving world, change detection is of prime importance to keep updated maps. To better sense areas with complex geometry (urban areas in particular), considering 3D data appears to be an interesting alternative to classical 2D images.
Externí odkaz:
http://arxiv.org/abs/2305.05421
Autor:
de Gélis, Iris, Saha, Sudipan, Shahzad, Muhammad, Corpetti, Thomas, Lefèvre, Sébastien, Zhu, Xiao Xiang
Publikováno v:
ISPRS Open Journal of Photogrammetry and Remote Sensing Volume 9, August 2023, 100044
Change detection from traditional \added{2D} optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud \added{from photogrammetry or LiDAR surveying} can fill this gap by provi
Externí odkaz:
http://arxiv.org/abs/2305.03529
Change detection is an important task that rapidly identifies modified areas, particularly when multi-temporal data are concerned. In landscapes with a complex geometry (e.g., urban environment), vertical information is a very useful source of knowle
Externí odkaz:
http://arxiv.org/abs/2304.12639
Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be due to the lack of dedicated large-scale annotated
Externí odkaz:
http://arxiv.org/abs/2206.03778
Autor:
Osio, Anne Achieng, Lê, Hoàng-Ân, Ayugi, Samson, Onyango, Fred, Odwe, Peter, Lefèvre, Sébastien
Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tr
Externí odkaz:
http://arxiv.org/abs/2204.07096