# Core Building Blocks: Next Gen Geo Spatial GPT Application

arXiv: https://arxiv.org/abs/2310.11029 · HTML: https://mlsanity.com/paper/2310.11029/

## 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 (most cited first, 30 of 32)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [How Language Model Hallucinations Can Snowball](https://mlsanity.com/paper/2305.13534/) (2023)
- [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023)
- [Language Model Tokenizers Introduce Unfairness Between Languages](https://mlsanity.com/paper/2305.15425/) (2023)
- [Sources of Hallucination by Large Language Models on Inference Tasks](https://mlsanity.com/paper/2305.14552/) (2023)
- [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://mlsanity.com/paper/2104.08704/) (2021)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [GPT4GEO: How a Language Model Sees the World's Geography](https://mlsanity.com/paper/2306.00020/) (2023)
- [Video Graph Transformer for Video Question Answering](https://mlsanity.com/paper/2207.05342/) (2022)
- [Impact of Tokenization on Language Models: An Analysis for Turkish](https://mlsanity.com/paper/2204.08832/) (2022)
- [Very Deep Transformers for Neural Machine Translation](https://mlsanity.com/paper/2008.07772/) (2020)
- [G-Transformer for Document-level Machine Translation](https://mlsanity.com/paper/2105.14761/) (2021)
- [GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT](https://mlsanity.com/paper/2307.07930/) (2023)
- [HEGEL: Hypergraph Transformer for Long Document Summarization](https://mlsanity.com/paper/2210.04126/) (2022)
- [Sample Efficient Text Summarization Using a Single Pre-Trained Transformer](https://mlsanity.com/paper/1905.08836/) (2019)
- [Optimizing Transformer for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/2011.02266/) (2020)
- [XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders](https://mlsanity.com/paper/2012.15547/) (2020)
- [Block-Skim: Efficient Question Answering for Transformer](https://mlsanity.com/paper/2112.08560/) (2021)
- [Pretrained Transformers for Simple Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2001.11985/) (2020)
- [BIM-GPT: a Prompt-Based Virtual Assistant Framework for BIM Information Retrieval](https://mlsanity.com/paper/2304.09333/) (2023)
- [Automated News Summarization Using Transformers](https://mlsanity.com/paper/2108.01064/) (2021)
- [Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations](https://mlsanity.com/paper/2307.03678/) (2023)
- [Transformer-Based Models for Question Answering on COVID19](https://mlsanity.com/paper/2101.11432/) (2021)
- [Training and Evaluation of a Multilingual Tokenizer for GPT-SW3](https://mlsanity.com/paper/2304.14780/) (2023)
- [Applying Transformer-based Text Summarization for Keyphrase Generation](https://mlsanity.com/paper/2209.03791/) (2022)
- [GANmapper: geographical data translation](https://mlsanity.com/paper/2108.04232/) (2021)
- [LittleBird: Efficient Faster & Longer Transformer for Question Answering](https://mlsanity.com/paper/2210.11870/) (2022)

## Built on by (most cited first, 4 of 4)

- [Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations](https://mlsanity.com/paper/2505.17136/) (2025)
- [A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems](https://mlsanity.com/paper/2401.10279/) (2024)
- [Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels](https://mlsanity.com/paper/2407.01846/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)

## 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.