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

arXiv: https://arxiv.org/abs/2511.06300 · HTML: https://mlsanity.com/paper/2511.06300/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 83 references that are in the map.
- First posted on arXiv 9 Nov 2025.

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [Entity Matching using Large Language Models](https://mlsanity.com/paper/2310.11244/) (2023)
- [ZeroER: Entity Resolution using Zero Labeled Examples](https://mlsanity.com/paper/1908.06049/) (2019)
- [Automated 3D reconstruction of LoD2 and LoD1 models for all 10 million buildings of the Netherlands](https://mlsanity.com/paper/2201.01191/) (2021)
- [AutoBlock: A Hands-off Blocking Framework for Entity Matching](https://mlsanity.com/paper/1912.03417/) (2019)
- [PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching](https://mlsanity.com/paper/2207.04802/) (2022)
- [Deep Indexed Active Learning for Matching Heterogeneous Entity Representations](https://mlsanity.com/paper/2104.03986/) (2021)
- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [CityJSON: a compact and easy-to-use encoding of the CityGML data model](https://mlsanity.com/paper/1902.09155/) (2019)
- [A Survey of Blocking and Filtering Techniques for Entity Resolution](https://mlsanity.com/paper/1905.06167/) (2019)
- [A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse](https://mlsanity.com/paper/2108.02800/) (2021)
- [SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines](https://mlsanity.com/paper/2303.03132/) (2023)
- [The Battleship Approach to the Low Resource Entity Matching Problem](https://mlsanity.com/paper/2311.15685/) (2023)

## Built on by (most cited first, 1 of 1)

- [Labeling Training Data for Entity Matching Using Large Language Models](https://mlsanity.com/paper/2606.28823/) (2026)

## Abstract (arXiv)

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.