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Application of machine learning for NDVI-based erosion zone segmentation

Year: 2025

Type: article

Abstract: This paper discusses the application of machine learning methods for the automatic detection of erosion zones based on the NDVI index. The U-Net model with the EfficientNetB0 pre-trained encoder is used, which allows us to achieve high segmentation accuracy. The study includes the preparation and analysis of geospatial data, model training, and testing on data from the region of South Kazakhstan. The developed system demonstrates an accuracy of 99.99%, which confirms the effectiveness of the proposed methodology. The obtained results can be used for monitoring soil degradation and taking measures to prevent erosion processes.

Source: International Journal of Innovative Research and Scientific Studies

DOI: 10.53894/ijirss.v8i3.7021

Authors: Mukhammed Bolsynbek, Gulzira Abdikerimova, Zhazira Taszhurekova, Rysty Tazhiyeva, Madi Akhmetzhanov

Institutions: L. N. Gumilyov Eurasian National University, M. Kh Dulati Taraz State University

Language: English

Topic: Soil erosion and sediment transport

Subfield: Soil Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Life on land, Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4410375998

FWCI:

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