HSC-GPT: A Large Language Model for Human Settlements Construction

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

Enhancing Urban Landscape Design: A GAN-Based Approach for Rapid Color Rendering of Park Sketches

In urban ecological development, the effective planning and design of living spaces are crucial. Traditional color plan rendering methods, mainly using generative adversarial networks (GANs), rely heavily on edge extraction. This often leads to the loss of important details from hand-drawn drafts, significantly affecting the portrayal of the designer’s key concepts. This issue is especially critical in complex park planning. To address this, our study introduces a system based on conditional GANs. This system rapidly converts black-and-white park sketches into comprehensive color designs. We also employ a data augmentation strategy to enhance the quality of the output. The research reveals: (1) Our model efficiently produces designs suitable for industrial applications. (2) The GAN-based data augmentation improves the data volume, leading to enhanced rendering effects. (3) Our unique approach of direct rendering from sketches offers a novel method in urban planning and design. This study aims to enhance the rendering aspect of an intelligent workflow for landscape design. More efficient rendering techniques will reduce the iteration time of early design solutions and promote the iterative speed of designers’ thinking, thus improving the speed and efficiency of the whole design process