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This article summarizes the findings of a study that was conducted on change discovery using machine literacy algorithms within the environment of Google Earth Engine (GEE). The study concentrated on the Bharatpur District, which is located in the state of Rajasthan in India. The exploration makes use of data collected through remote seeing and ways grounded on machine literacy to identify land cover changes over a particular period of time. Using the broad capabilities of GEE, a detailed study is carried out in order to identify and classify important changes in the geography. These changes include civic expansion, agrarian shifts, and the deterioration of natural territories. The study maps and quantifies changes in land cover through the operation of machine literacy algorithms. This will give significant perceptivity on the dynamics of land use within the region. The findings add to a more in-depth understanding of environmental transitions and enable informed sustainable land operation decisions and development plans in the Bharatpur District and other places that are similar.

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Change Detection Using Machine Learning Algorithms in Google Earth Engine Environment Bharatpur District of the Rajasthan State

  • 会议论文

  • 发表日期：

    2025年

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  • Gaurav Sharma,
  • Manoj Kumar Sharma

摘要

This article summarizes the findings of a study that was conducted on change discovery using machine literacy algorithms within the environment of Google Earth Engine (GEE). The study concentrated on the Bharatpur District, which is located in the state of Rajasthan in India. The exploration makes use of data collected through remote seeing and ways grounded on machine literacy to identify land cover changes over a particular period of time. Using the broad capabilities of GEE, a detailed study is carried out in order to identify and classify important changes in the geography. These changes include civic expansion, agrarian shifts, and the deterioration of natural territories. The study maps and quantifies changes in land cover through the operation of machine literacy algorithms. This will give significant perceptivity on the dynamics of land use within the region. The findings add to a more in-depth understanding of environmental transitions and enable informed sustainable land operation decisions and development plans in the Bharatpur District and other places that are similar.