# Toward building next-generation Geocoding systems: a systematic review

arXiv: https://arxiv.org/abs/2503.18888 · HTML: https://mlsanity.com/paper/2503.18888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 115 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages](https://mlsanity.com/paper/2310.09340/) (2023)
- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Did the Model Change? Efficiently Assessing Machine Learning API Shifts](https://mlsanity.com/paper/2107.14203/) (2021)
- [Leveraging Subword Embeddings for Multinational Address Parsing](https://mlsanity.com/paper/2006.16152/) (2020)
- [Geo-Text Data and Data-Driven Geospatial Semantics](https://mlsanity.com/paper/1809.05636/) (2018)
- [Is ChatGPT a game changer for geocoding -- a benchmark for geocoding address parsing techniques](https://mlsanity.com/paper/2310.14360/) (2023)

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

- [ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs](https://mlsanity.com/paper/2604.21357/) (2026)

## Abstract (arXiv)

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 characterizes the technical requirements for next-generation geocoding inputs and outputs. We then decompose the geocoding workflow into modular functional units and survey existing implementations. For each component, we identify methodological limitations, articulate domain-specific research questions and hypotheses, and outline evaluation strategies needed. Finally, we identify opportunities to improve next-generation geocoding systems in light of recent technological advances. We envision that this review provides a technical foundation and research agenda for advancing the design, assessment, and deployment of next-generation geocoding systems.