Belkadi, W.H.Drias, Y.Drias, H.Bouchelkia, H.Hamdous, S.2025-11-052025-11-0520259798331535629https://doi.org/10.1109/ACDSA65407.2025.11166348https://hdl.handle.net/20.500.11779/3116Antalya Bilim University; IEEEAccurate soil type mapping is vital for sustainable agriculture and land management. Yet, Algeria remains under-represented in global soil databases. To address this, we propose a pipeline combining Generative AI for data extraction with unsupervised learning for soil classification. After harmonizing Algerian soil data, we evaluate four clustering algorithms - K-means, DBSCAN, HDBSCAN, and Self-Organizing Maps - under various preprocessing settings. Internal and external metrics guide model selection. K-means and DBSCAN produced the most coherent clusters, while SOM best aligned with FAO soil types. RuleFit was then used to extract interpretable rules defining each cluster. This work highlights the potential of AI-based, interpretable clustering for digital soil mapping in data-scarce regions like Algeria. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessClusteringDBSCANDigital Soil MappingGenerative AIHDBSCANK-MeansRuleFitSelf-Organizing MapsConference Object10.1109/ACDSA65407.2025.111663482-s2.0-105018470117