# The Role of Open-Source LLMs in Shaping the Future of GeoAI

arXiv: https://arxiv.org/abs/2504.17833 · HTML: https://mlsanity.com/paper/2504.17833/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models](https://mlsanity.com/paper/2406.01584/) (2024)
- [Security and Privacy Challenges of Large Language Models: A Survey](https://mlsanity.com/paper/2402.00888/) (2024)
- [Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses](https://mlsanity.com/paper/2012.10544/) (2020)
- [Unveiling Privacy Risks in LLM Agent Memory](https://mlsanity.com/paper/2502.13172/) (2025)
- [K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization](https://mlsanity.com/paper/2306.05064/) (2023)
- [On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective](https://mlsanity.com/paper/2502.14296/) (2025)
- [Can Large Vision Language Models Read Maps Like a Human?](https://mlsanity.com/paper/2503.14607/) (2025)
- [GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis](https://mlsanity.com/paper/2411.03205/) (2024)
- [An Autonomous GIS Agent Framework for Geospatial Data Retrieval](https://mlsanity.com/paper/2407.21024/) (2024)
- [Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflows](https://mlsanity.com/paper/2504.04319/) (2025)
- [OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2503.16326/) (2025)
- [NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs](https://mlsanity.com/paper/2410.09129/) (2024)
- [MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model](https://mlsanity.com/paper/2503.15502/) (2025)
- [TAGIFY: LLM-powered Tagging Interface for Improved Data Findability on OGD portals](https://mlsanity.com/paper/2407.18764/) (2024)

## Built on by (most cited first, 2 of 2)

- [GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis](https://mlsanity.com/paper/2604.13888/) (2026)
- [Natural Language Interfaces for Spatial and Temporal Databases: A Comprehensive Overview of Methods, Taxonomy, and Future Directions](https://mlsanity.com/paper/2603.23375/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are transforming geospatial artificial intelligence (GeoAI), offering new capabilities in data processing, spatial analysis, and decision support. This paper examines the open-source paradigm's critical role in this transformation. While proprietary LLMs offer accessibility, they often limit the customization, interoperability, and transparency vital for specialized geospatial tasks. Conversely, open-source alternatives significantly advance Geographic Information Science (GIScience) by fostering greater adaptability, reproducibility, and community-driven innovation. Open frameworks empower researchers to tailor solutions, integrate cutting-edge methodologies (e.g., reinforcement learning, advanced spatial indexing), and align with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. However, the growing reliance on any LLM necessitates careful consideration of security vulnerabilities, ethical risks, and robust governance for AI-generated geospatial outputs. This paper argues that GIScience advances best not through a single model type, but by cultivating a diverse, interoperable ecosystem combining open-source foundations for innovation, custom geospatial models, and interdisciplinary collaboration. By critically evaluating the opportunities and challenges of open-source LLMs within the broader GeoAI landscape, this work contributes to a thorough discourse on leveraging LLMs to effectively advance spatial research, policy, and decision-making in an equitable, sustainable, and scientifically rigorous manner.