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Related Experiment Video

Updated: Jun 20, 2026

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

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Published on: August 7, 2017

A Multi-Scale Global Fusion-Based Method for Surface Fissure Extraction from UAV Imagery.

Mingxi Zhou¹, Min Ji^1,2,3,4, Fengxiang Jin^1,2

¹College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This summary is machine-generated.

A new MGF-UNet model accurately extracts ground fissures, crucial for geohazard monitoring and environmental protection in deforming areas. This method enhances safety and aids ecological restoration efforts.

Keywords:

UAV remote sensing deep learning multi-scale features semantic segmentation surface fissures

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Area of Science:

Geosciences and Remote Sensing
Artificial Intelligence in Earth Observation

Background:

Ground fissures in deforming areas pose significant risks to operational safety and the natural environment.
Elongated morphologies and large-scale variations of fissures present challenges for accurate feature extraction.

Purpose of the Study:

To develop an advanced semantic segmentation network, MGF-UNet, for accurate automatic fissure extraction.
To improve the detection and characterization of ground fissures in complex terrains.

Main Methods:

Integration of multi-scale feature sensing (MFS) and grouped efficient multi-scale attention (EMA) in shallow layers.
Utilizing a Token-Selective Context Transformer (TSCT) for selective global modeling of semantic features in deeper layers.
Employing feature-wise linear modulation (FiLM) for cross-level interaction and a Fourier transform-based adaptive feature fusion (AFF) module in the decoder.

Main Results:

MGF-UNet achieved 78.2% accuracy, 81.4% Dice score, and 68.6% IoU on benchmark datasets (MFD and road-based).
The proposed network outperformed existing mainstream networks in fissure extraction tasks.
Demonstrated effectiveness in suppressing background noise and enhancing boundary contrast for elongated fissures.

Conclusions:

MGF-UNet offers an effective solution for automatic fissure extraction in deformation-prone environments.
The method has significant potential for enhancing geohazard monitoring and supporting ecological restoration initiatives.