Drias, YassineDrias, HabibaBelkadi, Widad HassinaCakar, TunaAbdelhamid, ZakariaBensemmane, Riad Yacine2025-09-052025-09-0520259783031985645978303198565297898196523729783031931055978303195016297830319476989783032004406978303191007497830319261059789819639410978303197984297830319310242367-33702367-3389https://doi.org/10.1007/978-3-031-98565-2_72This study presents a methodology for generating highresolution digital soil maps by integrating artificial intelligence (AI) with geospatial technologies. The research begins with comprehensive data collection and the extraction of relevant soil properties with the help of generative AI. To improve predictive accuracy, ensemble learning algorithms were employed due to their ability to capture complex relationships within soil characteristics. Additionally, a structured preprocessing pipeline was developed to refine and standardize the collected soil data, ensuring its suitability for modeling. The model's performance was evaluated using spatial cross-validation techniques to identify the most effective predictive approach. To validate the proposed methodology, experiments were conducted in northern Algeria, a region characterized by diverse landscapes ranging from arid zones to fertile plains. The results demonstrate the potential of AI-driven approaches to enhance soil mapping, particularly in regions where high-quality and up-to-date soil data are limited. This study underscores the efficacy of AI and geospatial technologies in advancing precision agriculture and land management.eninfo:eu-repo/semantics/closedAccessDigital Soil MappingEnsemble LearningGeospatial TechnologiesGenerative AIArid RegionsArtificial IntelligenceLearning AlgorithmsPrecision AgricultureSoil SurveysSoilsData CollectionHigh ResolutionLearning TechnologySoil DataSoil MapsSoil PropertyMappingConference Object10.1007/978-3-031-98565-2_722-s2.0-105013081275