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Updated: Apr 23, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Sen2GF3Floods: A Benchmark Multi-Source Flood Dataset with Dual-Temporal and Active Learning Annotation.

Wenting Chen¹, Yueqin Zhu^1,2, Wenlong Han¹

¹National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, 100085, China.

Scientific Data

|February 26, 2026

Summary

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Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

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A new dataset, Sen2GF3Floods, combines Sentinel-2 and Gaofen-3 imagery for improved flood detection. This resource aids in developing advanced flood mapping algorithms for disaster response.

Area of Science:

Earth Observation
Remote Sensing
Geospatial Science

Background:

Flood disasters significantly impact global socioeconomic systems and ecosystems.
Existing flood detection datasets face limitations in source diversity, annotation precision, and scale.
High-quality datasets are crucial for advancing flood detection capabilities.

Purpose of the Study:

To introduce Sen2GF3Floods, the first dataset integrating pre-disaster Sentinel-2 optical imagery with post-disaster Gaofen-3 Synthetic Aperture Radar (SAR) imagery.
To develop a dual-temporal collaborative annotation framework using semi-automatic labeling and active learning to enhance accuracy and efficiency.
To provide a comprehensive resource for improving flood detection algorithms and supporting disaster response.

Main Methods:

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Last Updated: Apr 23, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Integration of Sentinel-2 optical and Gaofen-3 SAR imagery.
Development of a dual-temporal collaborative annotation framework incorporating semi-automatic labeling and U-Net++ based active learning.
Standardization of 21,483 samples from nine major flood events across diverse geomorphological settings.

Main Results:

Benchmark evaluations using five semantic segmentation models demonstrated robust flood mapping performance with multi-source fusion (Sentinel-2 RGB/NIR and GF-3 HH/HV data).
Active learning effectively reduced annotation costs while preserving data quality.
Models trained on Gaofen-3 SAR data exhibited good transferability to Sentinel-1 SAR data.

Conclusions:

Sen2GF3Floods is a valuable resource for advancing flood detection algorithms.
The dataset supports operational and real-time disaster response through improved flood mapping.
Multi-source data fusion and active learning are effective strategies for enhancing flood detection datasets.