A REVIEW OF GEOSPATIAL DATASETS AND SATELLITE IMAGERY FOR GEOAI APPLICATIONS: CHARACTERISTICS AND USE CASES
Keywords:
GeoAI, satellite image classification, geospatial datasets, remote sensing, deep learning, machine learning, GISAbstract
The rapid advancement of artificial intelligence (AI) has significantly transformed geospatial analysis, giving rise to GeoAI—a fusion of AI techniques with spatial sciences. This paper presents a comprehensive review of the integration of AI in satellite image classification and geospatial data analysis. It categorizes and compares key datasets such as aerial imagery, LiDAR, vector, and trajectory data, highlighting their unique characteristics and applications. Furthermore, it evaluates the performance of various machine learning and deep learning algorithms—including supervised, unsupervised, and CNN-based methods—applied to diverse classification tasks. Special emphasis is placed on dataset selection, accuracy metrics, and classification methodologies. The review also discusses key challenges such as data heterogeneity, scalability, and real-time processing, emphasizing the critical need for robust and scalable AI solutions in geospatial intelligence. The findings offer valuable insights for researchers and practitioners aiming to leverage AI for enhanced environmental monitoring, urban planning, and remote sensing.
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Copyright (c) 2025 Kuchkorov Temurbek Ataxonovich, Abdullayev Muxammadali Abdulaxad o‘g‘li, Uralova Iroda Abduvali qizi
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