HSC-GPT: A Large Language Model for Human Settlements Construction
Chen Ran Yao Xueqi Jiang Xuhui Han Zhengqi Guo Jingze Zhang Xianyue Lin Chunyu Liu Chumin Zhao Jing Lian Zeke Zhang Jingjing Li Keke
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.