3dSAGER: Geospatial Entity Resolution over 3D Objects (Technical Report)

2025-11-09

AI Summary

Addressing the entity matching challenge in multi-source 3D geospatial data—particularly when spatial proximity, textual metadata, or external identifiers are missing, unreliable, or inconsistent—this paper proposes 3dSAGER, an end-to-end framework. Methodologically, it integrates deep geometric feature extraction, learning-based candidate generation, 3D shape encoding, and similarity matching, trained end-to-end on real-world urban datasets. Its key contributions are: (1) a novel coordinate-system-agnostic 3D geometric feature representation, derived solely from intrinsic object shape structure; and (2) BKAFI, a lightweight, interpretable blocking method that enhances cross-source matching robustness and efficiency. Experiments demonstrate that 3dSAGER significantly outperforms strong baselines in both accuracy and inference speed. Ablation studies confirm the effectiveness and design rationale of each component.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Resolving geospatial entity mismatches across incompatible 3D datasets
• Addressing unreliable metadata in cross-source spatial data integration
• Enabling geometry-based matching without coordinate system alignment

Innovation

• Uses intrinsic geometry of 3D spatial objects
• Introduces spatial-reference-independent featurization mechanism
• Proposes lightweight BKAFI blocking method for efficiency