This article explores the potential for improving intelligent search through the integration of geospatial data and machine learning techniques. It reviews current approaches in the field of GEOINT, including the processing of satellite imagery, vector data, and crowd-sourced sources such as OpenStreetMap, along with the application of deep learning architectures (e.g., VGG16, U-Net) and anomaly detection algorithms (e.g., Isolation Forest, One-Class SVM). A comprehensive literature review is provided, highlighting the relevance of the topic and identifying a research gap stemming from the lack of a holistic interdisciplinary framework. In response, the article proposes an integrated methodology aimed at increasing the accuracy and interpretability of intelligent search systems. Based on empirical data derived from modern computational platforms and multimodal models, the study demonstrates that combining geospatial data with intelligent search algorithms opens new opportunities for building adaptive and high-precision analytical systems capable of responding quickly to dynamic environmental changes. The findings are of interest to professionals and researchers in geoinformatics and machine learning seeking to merge analytical methods to improve the performance of intelligent search systems with spatial data. Additionally, the approaches discussed may prove valuable in interdisciplinary research related to decision-making optimization in fields such as urban planning, logistics, and environmental monitoring.

geospatial intelligence, machine learning, intelligent search, deep learning, data integration, GEOINT, anomaly detection, semantic segmentation

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