Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Heidler, Konrad"'
The more than 200,000 glaciers outside the ice sheets play a crucial role in our society by influencing sea-level rise, water resource management, natural hazards, biodiversity, and tourism. However, only a fraction of these glaciers benefit from con
Externí odkaz:
http://arxiv.org/abs/2409.12034
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:
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
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
Autor:
Heidler, Konrad, Mou, Lichao, Hu, Di, Jin, Pu, Li, Guangyao, Gan, Chuang, Wen, Ji-Rong, Zhu, Xiao Xiang
Publikováno v:
International Journal of Applied Earth Observation and Geoinformation, Volume 116, 2023
Many current deep learning approaches make extensive use of backbone networks pre-trained on large datasets like ImageNet, which are then fine-tuned to perform a certain task. In remote sensing, the lack of comparable large annotated datasets and the
Externí odkaz:
http://arxiv.org/abs/2108.00688
Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-wo
Externí odkaz:
http://arxiv.org/abs/2104.11200
Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In
Externí odkaz:
http://arxiv.org/abs/2103.01849
Autor:
Zhao, Daixin, Heidler, Konrad, Asgarimehr, Milad, Arnold, Caroline, Xiao, Tianqi, Wickert, Jens, Zhu, Xiao Xiang, Mou, Lichao
Publikováno v:
In Remote Sensing of Environment 15 August 2023 294
Autor:
Li, Tian1,2 (AUTHOR) tian.li@bristol.ac.uk, Heidler, Konrad1 (AUTHOR), Mou, Lichao1 (AUTHOR), Ignéczi, Ádám2 (AUTHOR), Zhu, Xiao Xiang1,3 (AUTHOR), Bamber, Jonathan L.1,2 (AUTHOR)
Publikováno v:
Earth System Science Data. 2024, Vol. 16 Issue 2, p919-939. 21p.
Autor:
Heidler, Konrad, Mou, Lichao, Hu, Di, Jin, Pu, Li, Guangyao, Gan, Chuang, Wen, Ji-Rong, Zhu, Xiao Xiang
Publikováno v:
In International Journal of Applied Earth Observation and Geoinformation February 2023 116