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View article: An NSGA-II-XGBoost Machine Learning Approach for High-Precision Cropland Identification in Highland Areas: A Case Study of Xundian County, Yunnan, China
An NSGA-II-XGBoost Machine Learning Approach for High-Precision Cropland Identification in Highland Areas: A Case Study of Xundian County, Yunnan, China Open
Accurate identification of cultivated land in plateau and mountainous regions remains challenging due to complex terrain and the fragmented, small-scale distribution of farmland. This study develops a high-precision cropland identification…
View article: Identification of Abandoned Cropland and Global–Local Driving Mechanism Analysis via Multi-Source Remote Sensing Data and Multi-Objective Optimization
Identification of Abandoned Cropland and Global–Local Driving Mechanism Analysis via Multi-Source Remote Sensing Data and Multi-Objective Optimization Open
The issue of abandoned cropland poses a significant threat to national food security and the sustainable use of land resources, highlighting the urgent need for an efficient and interpretable remote sensing identification framework. This s…