HSC-GPT: A Large Language Model for Human Settlements Construction
This work addresses the problem of limited AI applicability in urban planning and landscape design for professionals, but it appears incremental as it adapts existing LLM approaches to a specific field.
The paper tackles the challenge of applying generative AI to human settlement construction tasks, which involve complex spatial details and diverse data, by proposing HSC-GPT, a large language model framework designed specifically for this domain.
The field of human settlement construction encompasses a range of spatial designs and management tasks, including urban planning and landscape architecture design. These tasks involve a plethora of instructions and descriptions presented in natural language, which are essential for understanding design requirements and producing effective design solutions. Recent research has sought to integrate natural language processing (NLP) and generative artificial intelligence (AI) into human settlement construction tasks. Due to the efficient processing and analysis capabilities of AI with data, significant successes have been achieved in design within this domain. However, this task still faces several fundamental challenges. The semantic information involved includes complex spatial details, diverse data source formats, high sensitivity to regional culture, and demanding requirements for innovation and rigor in work scenarios. These factors lead to limitations when applying general generative AI in this field, further exacerbated by a lack of high-quality data for model training. To address these challenges, this paper first proposes HSC-GPT, a large-scale language model framework specifically designed for tasks in human settlement construction, considering the unique characteristics of this domain.