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This study employs the object-oriented Cart (Classification and Regression Trees) decision tree methodology to delineate contiguous gravel areas within Zamu town. The primary objective is to devise a technique capable of efficiently identifying small-scale features on a macroscopic scale. Given the pervasive and uninterrupted distribution of background elements like forests, snow, and water in the designated study zone, the removal of these features can significantly bolster the precision of target feature extraction. The elimination of these background elements predominantly hinges on the application of index thresholds. Specifically, the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Snow Index (NDSI) are employed to filter out these non-target features. Following this, Google HD historical imagery is integrated with Sentinel-2 data to facilitate the object-oriented Cart decision tree-based feature extraction. Our findings underscore that both NDVI and NDWI are pivotal in eradicating forest backgrounds. For differentiating snow-covered terrains, the NDVI and NDSI indices prove particularly vital. The identification of water bodies necessitates the synergistic use of all three indices. Notably, the Cart decision tree approach, grounded in the &#x201C;cull, filter, classify, and merge&#x201D; philosophy, showcases superior classification accuracy relative to other supervised classification techniques. In the realm of decision tree rule formulation, spectral features dominate, constituting 47.6&#x0025; of the land class classification. Concurrently, texture features are instrumental, accounting for 38.1&#x0025;. These texture features exhibit an enhanced discriminatory capacity, whereas the incorporation of diverse indices offers limited incremental value. Pertinently, within the suite of features conducive to gravel extraction, both the Blue band and GLCM Entropy emerge as particularly efficacious.

Extraction Methods for Small-scale Features on a Large Scale: Investigating Object-oriented Cart Decision Tree for Gravel Information Extraction