Zobrazeno 1 - 10
of 3 691
pro vyhledávání: '"Landrieu P"'
Geospatial models must adapt to the diversity of Earth observation data in terms of resolutions, scales, and modalities. However, existing approaches expect fixed input configurations, which limits their practical applicability. We propose AnySat, a
Externí odkaz:
http://arxiv.org/abs/2412.14123
Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degree of ambiguity. However, existing approaches are deterministic and ove
Externí odkaz:
http://arxiv.org/abs/2412.06781
Autor:
Perron, Yohann, Sydorov, Vladyslav, Wijker, Adam P., Evans, Damian, Pottier, Christophe, Landrieu, Loic
Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced dee
Externí odkaz:
http://arxiv.org/abs/2412.05203
Autor:
Fogel, Fajwel, Perron, Yohann, Besic, Nikola, Saint-André, Laurent, Pellissier-Tanon, Agnès, Schwartz, Martin, Boudras, Thomas, Fayad, Ibrahim, d'Aspremont, Alexandre, Landrieu, Loic, Ciais, Philippe
Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the repro
Externí odkaz:
http://arxiv.org/abs/2407.09392
Autor:
Astruc, Guillaume, Dufour, Nicolas, Siglidis, Ioannis, Aronssohn, Constantin, Bouia, Nacim, Fu, Stephanie, Loiseau, Romain, Nguyen, Van Nguyen, Raude, Charles, Vincent, Elliot, XU, Lintao, Zhou, Hongyu, Landrieu, Loic
Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms. Yet, the absence of standard, large-scale, open-access datasets with reliably localizable
Externí odkaz:
http://arxiv.org/abs/2404.18873
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
Autor:
Wu, Sidi, Chen, Yizi, Mermet, Samuel, Hurni, Lorenz, Schindler, Konrad, Gonthier, Nicolas, Landrieu, Loic
Most image-to-image translation models postulate that a unique correspondence exists between the semantic classes of the source and target domains. However, this assumption does not always hold in real-world scenarios due to divergent distributions,
Externí odkaz:
http://arxiv.org/abs/2403.20142
We introduce a highly efficient method for panoptic segmentation of large 3D point clouds by redefining this task as a scalable graph clustering problem. This approach can be trained using only local auxiliary tasks, thereby eliminating the resource-
Externí odkaz:
http://arxiv.org/abs/2401.06704
Autor:
Garioud, Anatol, Gonthier, Nicolas, Landrieu, Loic, De Wit, Apolline, Valette, Marion, Poupée, Marc, Giordano, Sébastien, Wattrelos, Boris
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 provides a unique and rich resource for large-scale geospatial analysis. FLAI
Externí odkaz:
http://arxiv.org/abs/2310.13336
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 point clouds into a hierarchical superpoint structure, which makes our
Externí odkaz:
http://arxiv.org/abs/2306.08045