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Paddy Segmentation Using Google Earth Engine: A Remote Sensing Approach Abstract
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Authors
Prranith Prrranith 
Abstract
Paddy field segmentation using remote sensing is crucial for agricultural monitoring, yield prediction, and resource allocation. In this research, we employ Google Earth Engine (GEE) for paddy segmentation using Sentinel-2 satellite imagery. Our method leverages Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) to mask paddy fields efficiently. We collected 2000 images (masked and unmasked), trained a ResNet model achieving 91% accuracy, and implemented a real-time mobile application. This paper details the dataset preparation, masking methodology, implementation pipeline, and mobile app integration.
DOI
https://doi.org/10.31223/X5JF0V
Subjects
Education, Engineering, Life Sciences
Keywords
Paddy Segmentation, Google Earth Engine, remote sensing
Dates
Published: 2025-03-20 14:38
Last Updated: 2025-03-21 13:29
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Conflict of interest statement:
The author declares that there is no conflict of interest regarding the publication of this manuscript. No financial, personal, or professional relationships have influenced the research, analysis, or conclusions presented in this work.
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