Application of artificial intelligence and remote sensing in modern land degradation assessment
DOI:
https://doi.org/10.67484/mgc.13Keywords:
Land degradation, Land degradation assessment, Artificial intelligence, Remote sensing, Deep learningAbstract
Current land degradation assessment is carried out through the integration of geospatial technologies with field surveys and sampling to map the extent, scale, and distribution of six types of land degradation according to land parcels. The application of artificial intelligence (AI) enables enhanced analytical capabilities and accurate, detailed, and continuous prediction of land degradation status. This study analyzes the scientific basis for applying machine learning and deep learning models, trained on multi-source feature datasets such as satellite imagery, natural condition data, and soil sample analyses, to derive thematic data layers on indices and coefficients used in evaluating six types of land degradation. The research aims to guide methods for integrating remote sensing, GIS, and artificial intelligence data in building automated, regularly updated, and widely applicable land degradation prediction models.
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