I am a Ph.D. student (2018-2023, expected) at State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University. I received B.S. degree from the School of Geography and Information Engineering, China University of Geosciences, Wuhan, China, in 2018. I'm now a member of RSIDEA group, advised by Prof. Yanfei Zhong and Prof. Liangpei Zhang.
My research interest is in remote sensing visual perception and earth vision, especially multi-modal and multi-temporal remote sensing image analysis. My research goal is to design original and insightful Earth vision technologies to make high positive impacts on the geoscience field. Meanwhile, I am an enthusiast of remote sensing data science competitions.
Email: zhengzhuo [at] whu [dot] edu [dot] cn
2022,12, Awarded with the 16th Wuhan University Top Ten Academic Stars.
2022.10, Awarded with the 2022 Graduate Academic Innovation Outstanding Prize.
2021.12, Awarded with "Wang Zhizhuo Innovation Talent" Outstanding Prize.
2021.10, One paper is accepted by NeurIPS 2021 Datasets and Benchmarks.
2021.10, One paper is accepted by ISPRS P&RS.
2021.08, One paper is accepted by RSE.
2021.07, One paper is accepted by ICCV 2021.
2021.07, I win the 5th place in the Overhead Geopose Challenge hosted by NGA.
2021.03, Our team win the 4th place in 2021 IEEE GRSS Data Fusion Contest, Track: Multitemporal Semantic Change Detection.
2021.03, Our PE&RS paper wins the first place in the 2021 John I. Davidson President’s Award.
2020.12, One paper is accepted by ISPRS P&RS.
2020.11, the source code of FarSeg (CVPR 2020) has been available.
2020.10, Awarded with the 2020 Graduate Academic Innovation Outstanding Prize.
2020.06, I win the top graduate award at SpaceNet 6 & EarthVision workshop challenge at CVPR 2020.
2020.05, the source code of FPGA (TGRS 2020) has been available.
ChangeMask: Deep Multi-task Encoder-Transformer-Decoder Architecture for Semantic Change Detection
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Building Damage Assessment for Rapid Disaster Response with a Deep Object-based Semantic Change Detection Framework: from
natural disasters to man-made disasters
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Change is Everywhere: Single-Temporal Supervised Object Change Detection in
Remote Sensing Imagery
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LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
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Deep Multisensor Learning for Missing-Modality All-Weather Mapping
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Foreground-Aware Relation Network for Geospatial Object Segmentation in High
Spatial Resolution Remote Sensing Imagery
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FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End
Hyperspectral Image Classification
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HyNet: Hyper-scale object detection network framework for multiple spatial
resolution remote sensing imagery
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COLOR: Cycling, Offline Learning, and Online Representation Framework for
Airport and Airplane Detection Using GF-2 Satellite Images
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