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Ce Zhang

University of Bristol

University of Bristol,

Senate House, Tyndall Avenue

Bristol, BS8 ITH

United Kingdom

SCHOLARLY PAPERS

5

DOWNLOADS

181

TOTAL CITATIONS

0

Scholarly Papers (5)

1.

Parameter-Efficient Fine-Tuning of the Segment Anything Model for Remotesensing Sar Flood Mapping

Number of pages: 35 Posted: 31 Jul 2025
University of Bristol, University of Bristol, Lancaster University - Lancaster Environment Centre and University of Bristol
Downloads 62 (951,658)

Abstract:

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remote sensing, Flood Detection, SAR imagery, Segment Anything Model, PEFT

2.

Benchmarking Parameter-Efficient Fine-Tuning Strategies for Adapting Vision Foundation Models to SAR Flood Mapping

Number of pages: 25 Posted: 27 Dec 2025
University of Bristol, Lancaster University - Lancaster Environment Centre, University of Bristol and University of Bristol
Downloads 51 (1,063,489)

Abstract:

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Flood Detection, SAR imagery, Vision foundation models, PEFT

Evaluating the SWOT Mission for 3D-Flood Mapping Using Quality Flags

Number of pages: 45 Posted: 11 Feb 2026
University of Bristol, University of Bristol, University of Bristol and Lancaster University - Lancaster Environment Centre
Downloads 24 (1,486,852)

Abstract:

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SWOT, Water Surface Elevation, Quality Flag, remote sensing, Flood mapping

Evaluating the SWOT Mission for 3D-Flood Mapping Using Quality Flags

Number of pages: 44 Posted: 19 Mar 2026
University of Bristol, University of Bristol, University of Bristol and Lancaster University - Lancaster Environment Centre
Downloads 16 (1,579,999)

Abstract:

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SWOT, Water Surface Elevation, Quality Flag, remote sensing, Flood mapping

4.

From Flood Extent Mapping to Mechanism-Aware Flood Products: Integrating Flood Type Classification into Satellite-Based Flood Monitoring​

Number of pages: 21 Posted: 10 Mar 2026
University of Bristol, University of Bristol, Lancaster University - Lancaster Environment Centre and University of Bristol
Downloads 18 (1,481,165)

Abstract:

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Flood type, Flood mapping, Deep learning, CNN, Remote sensing, Flood risk management

5.

Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis

Number of pages: 28 Posted: 27 Jan 2026 Last Revised: 19 May 2026
University of Bristol, University of Bristol and University of Bristol
Downloads 10 (1,541,665)

Abstract:

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Vision-Language models, Multi-Task Learning, Change interpretation, LLM agents, Zero-shot change detection and captioning