Work

Comparison of random forest and extreme gradient boosting algorithms in land cover classification in Van Yen district, Yen Bai province, Vietnam

Year: 2025

Type: article

Abstract: Land cover classification using remote sensing data plays a crucial role in resource management and environmental monitoring.This study compares the performance of Random Forest (RF) and Extreme Gradient Boosting (XGBoost) in land cover classification in Van Yen District, Yen Bai Province, Vietnam.The input data includes Sentinel-1 radar images, Sentinel-2 optical images, and a total of 7,214 sample points collected for model development using the Google Colab platform.The results indicate that both RF and XGBoost achieve high performance, with overall accuracy (OA) ranging from 94.8% to 96.3% and Kappa coefficients between 0.936 and 0.955.Notably, RF demonstrates greater stability and higher accuracy than XGBoost in both scenarios: using Sentinel-2 alone and combining Sentinel-2 with Sentinel-1.This study provides a scientific basis for selecting appropriate algorithms and data to improve land cover classification efficiency in the region.

Source: Journal of Hydro-meteorology

DOI: 10.36335/vnjhm.2025(23).50-59

Authors: Thanh Dong Khuc, Ngoc Dung Luong, Dieu Hue Dang, Van Anh Tran

Institution: Hanoi University of Mining and Geology

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDGs: Life on land, Climate action

Open Access status: diamond

Funder: Center for the Environment, Harvard University