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A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active

Zhibao Wang1,2, Jie Zhang1, Lu Bai3

  • 1School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary

This study introduces an AI-powered framework for automatic remote sensing image change detection. It uses crowdsourcing and active learning to improve model accuracy and efficiency in land cover monitoring.

Keywords:
active learningchange detectioncrowdsourcinghuman-in-the-loopremote sensing

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Area of Science:

  • Earth and Space Sciences
  • Computer Science
  • Artificial Intelligence

Background:

  • Remote sensing change detection is crucial for land cover monitoring but traditional methods are slow and costly.
  • Existing methods struggle with limited annotated data, hindering model generalization and timely analysis.
  • Artificial intelligence offers potential for automating and improving the efficiency of change detection processes.

Purpose of the Study:

  • To develop an automatic change detection model integrated with a crowdsourcing collaborative framework.
  • To address the challenge of insufficient annotated data and weak model generalization in remote sensing change detection.
  • To enhance the efficiency and effectiveness of land cover change monitoring for natural resource departments.

Main Methods:

  • Proposed an automatic change detection model combined with a crowdsourcing collaborative framework.
  • Implemented human-in-the-loop technology and active learning for intelligent interpretation.
  • Developed a crowdsourcing quality control model to ensure annotation accuracy and annotator qualification.
  • Created a prototype platform integrating annotation, quality control, and change detection applications.

Main Results:

  • The human-machine collaborative intelligent interpretation method significantly improves upon traditional manual interpretation.
  • The framework effectively incorporates expert domain knowledge, reducing data annotation costs.
  • The crowdsourcing quality control model ensures reliable annotation results and annotator performance.
  • The developed platform provides an efficient and effective solution for land cover change monitoring.

Conclusions:

  • The proposed AI-driven framework offers a low-cost, high-efficiency solution for remote sensing image change detection.
  • This approach overcomes data scarcity issues, enhancing model generalization and performance.
  • The integrated platform empowers natural resource departments with advanced tools for effective land cover monitoring.