Urban environments are continuously mapped and modeled by various data collection platforms, including satellites, unmanned aerial vehicles and street cameras. The growing availability of 3D geospatial data from multiple modalities has introduced new opportunities and challenges for integrating spatial knowledge at scale, particularly in high-impact domains such as urban planning and rapid disaster management. Geospatial entity resolution is the task of identifying matching spatial objects across different datasets, often collected independently under varying conditions. Existing approaches typically rely on spatial proximity, textual metadata, or external identifiers to determine correspondence. While useful, these signals are often unavailable, unreliable, or misaligned, especially in cross-source scenarios. To address these limitations, we shift the focus to the intrinsic geometry of 3D spatial objects and present 3dSAGER (3D Spatial-Aware Geospatial Entity Resolution), an end-to-end pipeline for geospatial entity resolution over 3D objects. 3dSAGER introduces a novel, spatial-reference-independent featurization mechanism that captures intricate geometric characteristics of matching pairs, enabling robust comparison even across datasets with incompatible coordinate systems where traditional spatial methods fail. As a key component of 3dSAGER, we also propose a new lightweight and interpretable blocking method, BKAFI, that leverages a trained model to efficiently generate high-recall candidate sets. We validate 3dSAGER through extensive experiments on real-world urban datasets, demonstrating significant gains in both accuracy and efficiency over strong baselines. Our empirical study further dissects the contributions of each component, providing insights into their impact and the overall design choices.

Among the most critical features in smartphones are the ones related to the GNSS sensor positioning. Although the horizontal values usually deliver reliable results for most location-based services, the elevation value still suffers from reduced accuracy and reliability. This is mostly due to smartphone's related technological and physical limitations, together with environmental conditions that affect the GNSS observations. To overcome this problem, this paper suggests integrating measurements from various smartphone embedded sensors, supplemented with data from external mapping and environmental infrastructures. The use of deep learning models is investigated, aimed at improving the GNSS-based elevation. Evaluations show very promising results in enhancing the height accuracy, closer to the range of the horizontal positioning. This proves the capacity of improving smartphone's GNSS positioning capabilities for location-based services, with the focus on urban and concealed areas.

3D city models are valuable and useful information for many experts for a wide range of environmental applications, where building models are a major geospatial element. To produce detailed and accurate 3D building models, conventional authoritative production processes are used, mostly relying on aerial photogrammetry and laser scanners. Since these are not available in the open source domain, the majority of the geographic user-generated data, such as OpenStreetMap, are mostly limited to two-dimensional maps, where the 3rd(height) dimension is missing. To this end, we have developed a method that uses a single photograph taken from smartphones and OSM vector data to accurately calculate the photographed building height. When compared to reference precise field measurements, our implementation produced accurate building height values. This research presents a framework for the calculation of buildings height from user-generated photographs, which can be used for the creation of LoD1 building and city models.