Zobrazeno 1 - 10
of 1 151
pro vyhledávání: '"Zhu, Xiao Xiang"'
Autor:
Tiemann, Enno, Zhou, Shanyu, Kläser, Alexander, Heidler, Konrad, Schneider, Rochelle, Zhu, Xiao Xiang
Methane ($CH_4$) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide ($CO_2$) over 20 years, and it also acts as an air pollutant. Given its high radiative forcing potential and relatively short
Externí odkaz:
http://arxiv.org/abs/2408.15122
Autor:
Braham, Nassim Ait Ali, Albrecht, Conrad M, Mairal, Julien, Chanussot, Jocelyn, Wang, Yi, Zhu, Xiao Xiang
Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potential in hyperspectral imaging (HSI) remains untapped due to the absence
Externí odkaz:
http://arxiv.org/abs/2408.08447
We consider solving complex spatiotemporal dynamical systems governed by partial differential equations (PDEs) using frequency domain-based discrete learning approaches, such as Fourier neural operators. Despite their widespread use for approximating
Externí odkaz:
http://arxiv.org/abs/2407.11158
Monitoring changes triggered by mining activities is crucial for industrial controlling, environmental management and regulatory compliance, yet it poses significant challenges due to the vast and often remote locations of mining sites. Remote sensin
Externí odkaz:
http://arxiv.org/abs/2407.03971
Due to its cloud-penetrating capability and independence from solar illumination, satellite Synthetic Aperture Radar (SAR) is the preferred data source for large-scale flood mapping, providing global coverage and including various land cover classes.
Externí odkaz:
http://arxiv.org/abs/2406.04111
Land cover information is indispensable for advancing the United Nations' sustainable development goals, and land cover mapping under a more detailed category system would significantly contribute to economic livelihood tracking and environmental deg
Externí odkaz:
http://arxiv.org/abs/2406.00891
Self-supervised pretraining on large-scale satellite data has raised great interest in building Earth observation (EO) foundation models. However, many important resources beyond pure satellite imagery, such as land-cover-land-use products that provi
Externí odkaz:
http://arxiv.org/abs/2405.20462
Autor:
Zhu, Xiao Xiang, Xiong, Zhitong, Wang, Yi, Stewart, Adam J., Heidler, Konrad, Wang, Yuanyuan, Yuan, Zhenghang, Dujardin, Thomas, Xu, Qingsong, Shi, Yilei
Foundation models have enormous potential in advancing Earth and climate sciences, however, current approaches may not be optimal as they focus on a few basic features of a desirable Earth and climate foundation model. Crafting the ideal Earth founda
Externí odkaz:
http://arxiv.org/abs/2405.04285
We study the potential of noisy labels y to pretrain semantic segmentation models in a multi-modal learning framework for geospatial applications. Specifically, we propose a novel Cross-modal Sample Selection method (CromSS) that utilizes the class d
Externí odkaz:
http://arxiv.org/abs/2405.01217
Understanding how buildings are distributed globally is crucial to revealing the human footprint on our home planet. This built environment affects local climate, land surface albedo, resource distribution, and many other key factors that influence w
Externí odkaz:
http://arxiv.org/abs/2404.13911