Enhancing Search Intelligence with Geospatial Data and Machine Learning

Аннотация

This article explores the potential for ACCEPTED 20 April 2025 PUBLISHED 13 May 2025 improving intelligent search through the integration of VOLUME Vol.07 Issue 05 2025 geospatial data and machine learning techniques. It reviews current approaches in the field of GEOINT, CITATION including the processing of satellite imagery, vector Anna Deviatko. (2025). data, and crowd-sourced sources such as COPYRIGHT OpenStreetMap, along with the application of deep © 2025 Original content from this work may be used under the terms learning architectures (e.g., VGG16, U-Net) and of the creative commons attributes 4.0 License. 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. Keywords: geospatial intelligence, machine learning, The American Journal of Engineering and Technology 101 https://www.theamericanjournals.com/index.php/tajet intelligent search, deep learning, data integration, [4] introduce tools for combating misinformation using GEOINT, anomaly detection, semantic segmentation. machine learning to filter unreliable data and strengthen the reliability of news sources. In this context, Bhatt S. et al. [10] emphasize semantic Introduction: The integration of geospatial data and enrichment of input data using knowledge graphs, machine learning methods represents a promising significantly improving query interpretation in AI direction for improving information retrieval, decision systems and thereby enhancing their effectiveness. support, and resource management in fields such as national security, environmental monitoring, and urban Another line of research explores machine learning planning [2]. The use of high-resolution satellite applications in specific domains. Mungara S., Koganti S., imagery (e.g., Sentinel-2, Landsat) and open-source Chintakunta A. N., Kolluru V. K., and Nuthakki Y. [5] platforms such as OpenStreetMap provides a rich analyze consumer behavior in e-commerce, using foundation for analysis. When combined with modern analytical models to uncover hidden patterns machine learning algorithms, these data sources open influencing market dynamics. Wang J. et al. [9] trace the new possibilities for intelligent search systems [1]. evolution of machine learning over the past three decades, demonstrating how these methods have been The literature reveals several major research applied to optimize wireless networks, illustrating their directions, each making a substantial contribution to wide applicability beyond pure information retrieval the development of both geospatial analysis and tasks. intelligent search technologies. The first group of studies focuses on geospatial intelligence and data Equally important is the growing field of explainable AI. management. For example, Kolluru V. et al. [1] present Páez A. [8] calls for a pragmatic shift toward algorithmic a systematic review of current approaches for transparency, arguing that interpretability is essential improving geospatial intelligence using advanced data for integrating AI systems into critical information analytics and machine learning. Their work infrastructure. demonstrates how the use of large volumes of heterogeneous data can significantly enhance spatial In summary, contemporary literature reveals a certain analysis outcomes. In a similar vein, Breunig M. et al. [3] tension between technical and methodological explore the evolution of geodata management, approaches to integrating machine learning with highlighting major achievements and identifying future geospatial data. On one hand, the emphasis is on development opportunities in the face of emerging merging diverse data sources and optimizing challenges. A practical perspective is reflected in the algorithms for more accurate spatial and informational work of Feldmeyer D. et al. [6], who use analysis. On the other, there are methodological OpenStreetMap data and machine learning to generate discrepancies in how