Advances in geospatial analysis in the era of big data challenges and integrating GeoAI techniques

Advances in geospatial analysis

Abstract

The evolution of geospatial analysis from the mid‑20th century quantitative geography and regional science to contemporary GeoAI reflects a profound methodological and technological transformation. Early geospatial work relied on statistical and cartographic techniques implemented through geographic information systems (GIS) to map, query, and test hypothesis‑driven models of spatial distribution and relationships. The advent of geospatial big data driven by satellite remote sensing, Internet of Things sensors, mobile devices, and social media introduced unprecedented volume, velocity, and variety that exceeded the capacity of traditional, aggregate approaches and conventional computing infrastructure. In response to this, advances in high‑performance computing, parallel processing, GPU acceleration, and cloud platforms enabled scalable data storage and processing, while machine learning and deep learning methods were adapted to exploit spatial structure, multi‑scale phenomena, and heterogeneous data modalities. GeoAI thus emerged as the integrated application of artificial intelligence to geospatial big data, enabling automated feature extraction, detection of complex spatial and spatiotemporal patterns, and robust predictive modeling at scales previously unattainable. This review synthesizes recent advancements in deep learning architectures and earth observation data fusion to characterize the current state of GeoAI. While GeoAI substantially extends analytical capability, it also raises critical challenges: spatial autocorrelation and scale effects, data quality and representativeness, model interpretability and uncertainty quantification, computational cost, and ethical concerns including privacy and bias. Addressing these issues through theory‑informed methods, transparent model design and rigorous data governance will be essential for realizing GeoAI’s potential in research and applied domains.

Keywords: Spatial analysis, big data, GEOAI, quantitative geography, regional studies