Recent Advances in Geospatial Data Management and Analytics

Dear Colleagues,

In recent years, geospatial science has experienced pronounced development driven by advances in remote sensing, unmanned aerial systems, LiDAR technologies, radar measurements, and increasingly dense networks of in situ sensors. These developments have substantially expanded both the volume and diversity of available spatial data, opening new avenues for observing environmental and urban systems at high spatial and temporal resolutions. At the same time, progress in geospatial artificial intelligence (GeoAI), machine learning, multimodal data integration, and cloud-based computation has strengthened the analytical capacity of the field, enabling more sophisticated modelling of natural processes and human–environment interactions.

Despite this progress, the integration and interpretation of heterogeneous geospatial datasets remain major challenges. Differences in spatial scale, sensor characteristics, temporal frequency, and semantic structure complicate the development of unified analytical frameworks, while domain shift continues to limit the transferability of predictive algorithms. The lack of benchmark datasets and standardized validation protocols further restricts reproducibility, particularly in research related to climate change, natural hazards, environmental degradation, water-resource management, and complex urban systems.

This Special Issue brings together contributions that directly address these challenges and propose conceptually and methodologically advanced solutions. The collected papers span varied topics, such as GeoAI and machine-learning approaches, multimodal data fusion, real-time geospatial big-data management, ecological and geomorphological modelling, hydrological risk analysis, and urban analytics.

Recent trends particularly highlight the growing importance of multisensory approaches, which enable more comprehensive observation of environmental and urban processes, ranging from geomorphological change and coastal dynamics to ecosystem conditions, flood-risk assessment, and applications in precision agriculture. The integration of UAV photogrammetry, LiDAR, multispectral and hyperspectral imagery, radar-based water-level measurements, GNSS observations, and field data supports the development of detailed terrain and surface-process models, thereby increasing the reliability of simulations and the effectiveness of early-warning systems. Building on these sensing frameworks, multimodal AI models integrate heterogeneous data sources within unified analytical structures, enabling deeper interpretation of climate-induced changes and the development of more robust predictive analyses.

By presenting these interconnected perspectives, this Special Issue will highlight the most recent advances in geospatial data management and analytics and promote the development of robust, scalable, and transparent methodologies essential for addressing environmental and societal challenges in the decades ahead. 

Prof. Dr. Ante Šiljeg
Prof. Dr. Mladen Jurišić
Dr. Silvija Šiljeg
Guest Editors

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