Qi Wang
Tsinghua University
9 Papers
Qi Wang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 2, co-authored 2 publications.
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Papers
The first all-season sample set for mapping global land cover with Landsat-8 data
Congcong Li,Congcong Li,Peng Gong,Jie Wang,Zhiliang Zhu,Gregory S. Biging,Cui Yuan,Tengyun Hu,Haiying Zhang,Qi Wang,Xuecao Li,Xiaoxuan Liu,Yidi Xu,Jing Guo,Caixia Liu,Kwame Oppong Hackman,Meinan Zhang,Yuqi Cheng,Le Yu,Jun Yang,Huabing Huang,Nicholas Clinton +21 more
TL;DR: In this article, the authors reported the first all-season training and validation sample sets for global land cover classification with Landsat-8 data, and compared the performances of training samples in different seasons using Random Forest algorithm.
159
Efficient Inductive Vision Transformer for Oriented Object Detection in Remote Sensing Imagery
TL;DR: Zhang et al. as mentioned in this paper proposed an efficient inductive vision Transformer framework for oriented object detection in remote sensing imagery, which follows the hierarchical feature pyramid structure and makes threefold contributions, as follows.
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GCWNet: A Global Context-Weaving Network for Object Detection in Remote Sensing Images
TL;DR: Zhang et al. as mentioned in this paper proposed a novel global context weaving network (GCWNet) for object detection in remote sensing images, which assembles a global context with high-level and low-level features through feature weaving.
37
An all-season sample database for improving land-cover mapping of Africa with two classification schemes
Congcong Li,Peng Gong,Jie Wang,Cui Yuan,Tengyun Hu,Qi Wang,Le Yu,Nicholas Clinton,Mengna Li,Jing Guo,Duole Feng,Conghong Huang,Zhicheng Zhan,Xiaoyi Wang,Bo Xu,Yaoyu Nie,Kwame Oppong Hackman +16 more
TL;DR: This work produces the first training and validation data sets with seasonal labels in order to support the production of seasonal land-cover data for entire Africa and for the first time, two classification systems were created for the same set of samples.
27
Remote Sensing Scene Classification Based on Attention-Enabled Progressively Searching
TL;DR: A multi-stage network progressive fusion search method, which discards useless operations in stages, reduces the burden of search algorithm and improves the search efficiency, is proposed and the experimental results show that the proposed method performs better than the manual method and the current neural network architecture search method.
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