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This paper introduces LLM-Geo, a framework integrating the open-source DeepSeek-Coder model (specifically the 1.3B parameter version) into a GIS platform called DS-GeoAI, to address limitations of commercial LLM-based GIS solutions. The framework aims to reduce costs and increase accessibility by eliminating API dependencies and enabling local deployment. The DS-GeoAI platform achieves 90% accuracy in generating Python code for spatial analysis tasks after automated debugging, demonstrating comparable performance to commercial solutions with significantly lower operational costs.
A 1.3B parameter open-source LLM can rival commercial GIS solutions in spatial analysis accuracy, while slashing costs by 99%.
Large language models (LLMs) have paved a way for geographic information system (GIS) that can solve spatial problems with minimal human intervention. However, current commercial LLM-based GIS solutions pose many limitations for researchers, such as proprietary APIs, high operational costs, and internet connectivity requirements, making them inaccessible in resource-constrained environments. To overcome this, this paper introduced the LLM-Geo framework with the DS-GeoAI platform, integrating the DeepSeek-Coder model (the open-source, lightweight version deepseek-coder-1.3b-base) running directly on Google Colab. This approach eliminates API dependence, thus reducing deployment costs, and ensures data independence and sovereignty. Despite having only 1.3 billion parameters, DeepSeek-Coder proved to be highly effective: generating accurate Python code for complex spatial analysis, achieving a success rate comparable to commercial solutions. After an automated debugging step, the system achieved 90% accuracy across three case studies. With its strong error- handling capabilities and intelligent sample data generation, DS-GeoAI proves highly adaptable to real-world challenges. Quantitative results showed a cost reduction of up to 99% compared to API-based solutions, while expanding access to advanced geo-AI technology for organizations with limited resources.
