no code implementations • 7 May 2024 • Xiao Xiang Zhu, Zhitong Xiong, Yi Wang, Adam J. Stewart, Konrad Heidler, Yuanyuan Wang, Zhenghang Yuan, Thomas Dujardin, Qingsong Xu, Yilei Shi
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.
1 code implementation • 17 Jan 2024 • Konrad Heidler, Ingmar Nitze, Guido Grosse, Xiao Xiang Zhu
To improve model generalization across the Arctic without the need for additional labelled data, we present a semi-supervised learning approach to train semantic segmentation models to detect RTS.
no code implementations • 7 Jul 2023 • Konrad Heidler, Lichao Mou, Erik Loebel, Mirko Scheinert, Sébastien Lefèvre, Xiao Xiang Zhu
Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps.
1 code implementation • 2 Aug 2021 • Konrad Heidler, Lichao Mou, Di Hu, Pu Jin, Guangyao Li, Chuang Gan, Ji-Rong Wen, Xiao Xiang Zhu
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.
Ranked #2 on Cross-Modal Retrieval on SoundingEarth
1 code implementation • 22 Apr 2021 • Yuansheng Hua, Lichao Moua, Jianzhe Lin, Konrad Heidler, Xiao Xiang Zhu
To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory.
5 code implementations • 2 Mar 2021 • Konrad Heidler, Lichao Mou, Celia Baumhoer, Andreas Dietz, Xiao Xiang Zhu
Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years.