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Related Experiment Video

Updated: Jul 19, 2025

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A Light-Weight Cropland Mapping Model Using Satellite Imagery.

Maya Haj Hussain1, Diaa Addeen Abuhani1, Jowaria Khan1

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.

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|August 12, 2023
PubMed
Summary

This study introduces a fast, cost-effective machine learning model for creating accurate cropland maps using satellite imagery. The approach enhances global crop production and food security assessments by providing reliable data efficiently.

Keywords:
Normalized Difference Vegetation Indexcropland extentcropland intensitymachine learningtime series

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Environmental Monitoring

Background:

  • Reliable cropland maps are essential for agriculture, natural resources, environment, health, and sustainability.
  • Manual cropland mapping is labor-intensive, costly, and time-consuming, hindering global food security studies.
  • Satellite imagery offers a potential solution for efficient cropland assessment.

Purpose of the Study:

  • To develop a cost-effective, fast, and accurate machine learning-based approach for cropland mapping.
  • To create a reliable model for assessing cropland extent and intensity using satellite data.
  • To address the limitations of manual data collection for agricultural monitoring.

Main Methods:

  • Utilized Sentinel-2 satellite imagery for four diverse test regions: Iran, Mozambique, Sri-Lanka, and Sudan.
  • Implemented a complete pipeline involving data collection and time series reconstruction.
  • Applied machine learning models for cropland extent and crop intensity mapping, incorporating NDVI (Normalized Difference Vegetation Index) scores.

Main Results:

  • Achieved high accuracy in cropland mapping across all four test regions.
  • Reported accuracy scores ranging from 0.92 to 0.98.
  • Demonstrated the effectiveness of the machine learning approach in diverse geographical contexts.

Conclusions:

  • The proposed machine learning approach provides a reliable and efficient method for cropland mapping.
  • This technique offers a cost-effective alternative to traditional manual mapping methods.
  • The model's high accuracy supports improved agricultural monitoring and food security assessments globally.