Zobrazeno 1 - 10
of 144
pro vyhledávání: '"Tarabalka, Yuliya"'
Publikováno v:
IGARSS2024, Jul 2024, ATHENE, Greece
The modern road network topology comprises intricately designed structures that introduce complexity when automatically reconstructing road networks. While open resources like OpenStreetMap (OSM) offer road networks with well-defined topology, they m
Externí odkaz:
http://arxiv.org/abs/2406.14941
Automatic methods for reconstructing buildings from airborne LiDAR point clouds focus on producing accurate 3D models in a fast and scalable manner, but they overlook the problem of delivering simple and regularized models to practitioners. As a resu
Externí odkaz:
http://arxiv.org/abs/2404.08104
Reconstructing urban areas in 3D out of satellite raster images has been a long-standing and challenging goal of both academical and industrial research. The rare methods today achieving this objective at a Level Of Details $2$ rely on procedural app
Externí odkaz:
http://arxiv.org/abs/2307.05409
We first exhibit a multimodal image registration task, for which a neural network trained on a dataset with noisy labels reaches almost perfect accuracy, far beyond noise variance. This surprising auto-denoising phenomenon can be explained as a noise
Externí odkaz:
http://arxiv.org/abs/2102.05262
Autor:
De Teyou, Gael Kamdem, Tarabalka, Yuliya, Manighetti, Isabelle, Almar, Rafael, Tripod, Sebastien
Many earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. In this work, we exploit both temporal and spatial in
Externí odkaz:
http://arxiv.org/abs/2008.08432
The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for a
Externí odkaz:
http://arxiv.org/abs/2005.06216
While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in
Externí odkaz:
http://arxiv.org/abs/2004.14875
Domain adaptation for semantic segmentation has recently been actively studied to increase the generalization capabilities of deep learning models. The vast majority of the domain adaptation methods tackle single-source case, where the model trained
Externí odkaz:
http://arxiv.org/abs/2004.06402
Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test da
Externí odkaz:
http://arxiv.org/abs/2002.05925
Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations. The large shift
Externí odkaz:
http://arxiv.org/abs/1907.12859