Bio: I am currently a postdoc at Stanford Artificial Intelligence Laboratory (SAIL), Department of Computer Science, Stanford University, working with Prof. Stefano Ermon, Prof. David Lobell, and Prof. Marshall Burke. I received my Ph.D. in Photogrammetry and Remote Sensing from Wuhan University in 2023, advised by Prof. Yanfei Zhong and Prof. Liangpei Zhang. I obtained B.S. degree from the School of Geography and Information Engineering, China University of Geosciences, Wuhan, China, in 2018.
My research interest is in Earth Vision and Simulation, especially multi-modal and multi-temporal remote sensing image
analysis.
My research goal is to design original and insightful GeoAI technologies to help solve societal and environmental challenges facing humanity, in pursuit of a sustainable future.
Meanwhile, I am an enthusiast of remote sensing data science competitions (commonly used ID: EVER), but now I have little time to play these games :(
Email: zhuozheng [at] cs [dot] stanford [dot] edu; zhengzhuo [at] whu [dot] edu [dot] cn
2024.10, Changen2 got accepted to IEEE TPAMI.
2024.10, Awarded with the 2024 Graduate Academic Innovation Outstanding Prize.
2024.09, Segment Any Change got accepted to NeurIPS 2024.
2024.09, selected for Stanford University's World Top 2% Scientists List (Geological & Geomatics Engineering) for 2024.
2024.09, STCA got accepted to RSE.
2024.06, DPCM and ChangeSparse got accepted to ISPRS P&RS.
2024.05, ChangeStar2 got accepted in IJCV.
2023.10, Urban Vehicle Segmentation (UV6K) dataset is publicly available. Use it in your study now!
2023.07, One paper is accepted by ICCV 2023.
2023.07, One paper is accepted by IEEE TPAMI.
2023.06, Awarded with Li Xiaowen Remote Sensing Young Scholar Award.
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
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Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model
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Segment Any Change
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Towards transferable building damage assessment via unsupervised single-temporal change adaptation
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Unifying Remote Sensing Change Detection via Deep Probabilistic Change Models: From Principles, Models to Applications
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Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection
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Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic
Change Process
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FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High
Spatial Resolution Remote Sensing Imagery
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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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