Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies
  1. Home
  3. Global-local Feature Fusion Network For Remote Sensing Image Change Detection In Open-pit Mining Areas.
  1. Home
  3. Global-local Feature Fusion Network For Remote Sensing Image Change Detection In Open-pit Mining Areas.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Metal-Free Induced Tandem Cyclization of 2‑Aryl‑<i>N</i>‑Methacryloyl Indoles via 1,2-Hydrogen Atom Transfer.

ACS omega·2026
Same author

Carbon dots promote cotton growth and development by enhancing photosynthesis and antioxidant enzyme activities.

BMC plant biology·2026
Same author

A Transcriptome Study on Seed Germination of <i>Nitraria roborowskii</i> Kom.

International journal of molecular sciences·2026
Same author

PE microplastics altered microbial resource limitation and C/N use efficiency in cotton rhizosphere soil.

Journal of hazardous materials·2026
Same author

Chromosomal-level genome assembly of two dominant desert shrub species in Haloxylon (Amaranthaceae).

Scientific data·2025
Same author

Metabolomic insights into rhizosphere microbial dynamics in desert plants under drought stress.

The Science of the total environment·2025
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Videos

Global-Local Feature Fusion Network for Remote Sensing Image Change Detection in Open-Pit Mining Areas.

Zhewen Zheng1, Jianjun Yang1, Guanghui Lv1

  • 1School of Ecology and Environment, Xinjiang University, Urumqi 830017, China.

Sensors (Basel, Switzerland)
|May 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Detecting changes in open-pit mining areas using remote sensing is crucial. The new Global-Local Multi-scale Cross-fusion Enhanced Change Detection Network (GLMECD-Net) effectively identifies complex mining alterations.

Keywords:
change detectionglobal–local feature fusionmining area ecological monitoringmulti-scale featuresremote sensing

Related Experiment Videos

Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Environmental Monitoring

Background:

  • Accurate change detection in open-pit mining areas is vital for supervision, ecological monitoring, and restoration.
  • Mining-related changes present challenges due to multi-scale patterns, irregular boundaries, and fragmented distributions.
  • Existing methods struggle to balance global context and local details, leading to incomplete boundary extraction and missed subtle changes.

Purpose of the Study:

  • To develop an advanced deep learning network for enhanced change detection in remote sensing imagery of open-pit mining areas.
  • To address limitations in existing methods regarding the perception of global context and local details.
  • To improve the accuracy of detecting complex and subtle changes, including boundary recovery.

Main Methods:

  • Proposed GLMECD-Net (Global-Local Multi-scale Cross-fusion Enhanced Change Detection Network) utilizing a Siamese encoder for bi-temporal feature extraction.
  • Introduced a Global-Local Feature Mixing Embedding (GLME) module to capture both long-range context and local spatial details.
  • Employed multi-scale feature aggregation and cross-temporal feature fusion for improved change representation.

Main Results:

  • The GLMECD-Net achieved 71.66% Precision, 83.78% Overall Accuracy (OA), 77.53% F1-score, and 53.82% Intersection over Union (IoU) on mining area datasets.
  • Demonstrated superior performance in detecting complex and subtle changes compared to existing methods.
  • Showcased effective boundary recovery and improved sensitivity to mining-induced alterations.

Conclusions:

  • GLMECD-Net offers an effective and robust solution for remote sensing-based change detection in open-pit mining environments.
  • The proposed network successfully balances global context and local detail perception for improved accuracy.
  • The findings support the application of GLMECD-Net for enhanced mining supervision and ecological management.