Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Saha, Sudipan"'
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the
Externí odkaz:
http://arxiv.org/abs/2411.03223
Autor:
Vora, Pratik, Saha, Sudipan
Semantic segmentation is an important topic in computer vision with many relevant application in Earth observation. While supervised methods exist, the constraints of limited annotated data has encouraged development of unsupervised approaches. Howev
Externí odkaz:
http://arxiv.org/abs/2408.07393
Autor:
Ahmad, Tahir, Saha, Sudipan
Change detection (CD) is a fundamental task in Earth observation. While most change detection methods detect all changes, there is a growing need for specialized methods targeting specific changes relevant to particular applications while discarding
Externí odkaz:
http://arxiv.org/abs/2408.06644
Autor:
Verma, Tushar, Saha, Sudipan
Satellite images have become increasingly valuable for modelling regional climate change effects. Earth surface forecasting represents one such task that integrates satellite images with meteorological data to capture the joint evolution of regional
Externí odkaz:
http://arxiv.org/abs/2408.05916
Autor:
Saha, Sudipan
Unsupervised transfer learning-based change detection methods exploit the feature extraction capability of pre-trained networks to distinguish changed pixels from the unchanged ones. However, their performance may vary significantly depending on seve
Externí odkaz:
http://arxiv.org/abs/2405.09896
With the global population on the rise, our cities have been expanding to accommodate the growing number of people. The expansion of cities generally leads to the engulfment of peripheral areas. However, such expansion of urban areas is likely to cau
Externí odkaz:
http://arxiv.org/abs/2401.03005
Precipitation nowcasting (up to a few hours) remains a challenge due to the highly complex local interactions that need to be captured accurately. Convolutional Neural Networks rely on convolutional kernels convolving with grid data and the extracted
Externí odkaz:
http://arxiv.org/abs/2309.05828
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
Autor:
Saha, Sudipan
Multi-temporal image analysis has been widely used in many applications such as urban monitoring, disaster management, and agriculture. With the development of the remote sensing technology, the new generation remote sensing satellite images with Hig
Externí odkaz:
http://hdl.handle.net/11572/263814
Autor:
Kondmann, Lukas, Toker, Aysim, Saha, Sudipan, Schölkopf, Bernhard, Leal-Taixé, Laura, Zhu, Xiao Xiang
Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this paper, we introduce Sibling Regression for Optical Change detection (SiROC), an unsupervised method for change detection in
Externí odkaz:
http://arxiv.org/abs/2110.02068