Toward building next-generation Geocoding systems: a systematic review

2025-03-24

AI Summary

To address the multifaceted requirements of scientific research and location-based services—particularly in geocoding accuracy, robustness, and semantic understanding—this paper systematically analyzes evolutionary drivers and deconstructs core functional modules, establishing for the first time an input–output requirements framework tailored to diverse application scenarios. We propose a novel multi-paradigm collaborative architecture integrating rule engines, information retrieval, named entity recognition, geographic knowledge graphs, and large language models (LLMs). Based on this, we formulate design principles and a technical roadmap for next-generation geocoding systems: extensibility, high robustness, and semantic awareness. Key contributions include identifying three LLM-driven breakthrough directions: context-aware address parsing, cross-modal spatial-semantic alignment, and dynamic knowledge-enhanced reasoning—providing a systematic methodology for both academia and industry.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

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

Abstract

Geocoding systems are widely used in both scientific research for spatial analysis and everyday life through location-based services. The quality of geocoded data significantly impacts subsequent processes and applications, underscoring the need for next-generation systems. In response to this demand, this review first examines the evolving requirements for geocoding inputs and outputs across various scenarios these systems must address. It then provides a detailed analysis of how to construct such systems by breaking them down into key functional components and reviewing a broad spectrum of existing approaches, from traditional rule-based methods to advanced techniques in information retrieval, natural language processing, and large language models. Finally, we identify opportunities to improve next-generation geocoding systems in light of recent technological advances.

Problem

• Addressing evolving requirements for geocoding inputs and outputs
• Analyzing key functional components for building geocoding systems
• Identifying improvement opportunities with recent technological advances

Innovation

• Systematic review of geocoding requirements
• Integration of NLP and large language models
• Advanced techniques for functional components