mlsanityData to 14 Oct 2026

Core Building Blocks: Next Gen Geo Spatial GPT Application

arXiv 2310.11029 ↗First posted 17 Oct 2023cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

This paper proposes MapGPT which is a novel approach that integrates the capabilities of language models, specifically large language models (LLMs), with spatial data processing techniques. This paper introduces MapGPT, which aims to bridge the gap between natural language understanding and spatial data analysis by highlighting the relevant core building blocks. By combining the strengths of LLMs and geospatial analysis, MapGPT enables more accurate and contextually aware responses to location-based queries. The proposed methodology highlights building LLMs on spatial and textual data, utilizing tokenization and vector representations specific to spatial information. The paper also explores the challenges associated with generating spatial vector representations. Furthermore, the study discusses the potential of computational capabilities within MapGPT, allowing users to perform geospatial computations and obtain visualized outputs. Overall, this research paper presents the building blocks and methodology of MapGPT, highlighting its potential to enhance spatial data understanding and generation in natural language processing applications.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 32 of its 39 references that are in the map. First posted on arXiv 17 Oct 2023.

Builds on

32 of 39 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
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  7. 2023Language Model Tokenizers Introduce Unfairness Between Languagescited by 214
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  11. 2023GPT4GEO: How a Language Model Sees the World's Geographycited by 68
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20 more
  1. 2022Impact of Tokenization on Language Models: An Analysis for Turkishcited by 62
  2. 2020Very Deep Transformers for Neural Machine Translationcited by 50
  3. 2021G-Transformer for Document-level Machine Translationcited by 46
  4. 2023GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPTcited by 41
  5. 2022HEGEL: Hypergraph Transformer for Long Document Summarizationcited by 28
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  11. 2023BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrievalcited by 12
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  13. 2023Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relationscited by 9
  14. 2021Transformer-Based Models for Question Answering on COVID19cited by 7
  15. 2023Training and Evaluation of a Multilingual Tokenizer for GPT-SW3cited by 7
  16. 2022Applying Transformer-based Text Summarization for Keyphrase Generationcited by 6
  17. 2021GANmapper: geographical data translationcited by 5
  18. 2022LittleBird: Efficient Faster & Longer Transformer for Question Answeringcited by 4
  19. 2023TranSFormer: Slow-Fast Transformer for Machine Translationcited by 4
  20. 2023Transformer Based Implementation for Automatic Book Summarizationcited by 2

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relationscited by 23
  2. 2024A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systemscited by 6
  3. 2024Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labelscited by 3
  4. 2025Urban Computing in the Era of Large Language Modelscited by 2