In traditional human living environment landscape design, the establishment of three-dimensional models is an essential step for designers to intuitively present the spatial relationships of design elements, as well as a foundation for conducting landscape analysis on the site. Rapidly and effectively generating beautiful and realistic landscape spaces is a significant challenge faced by designers. Although generative design has been widely applied in related fields, they mostly generate three-dimensional models through the restriction of indicator parameters. However, the elements of landscape design are complex and have unique requirements, making it difficult to generate designs from the perspective of indicator limitations. To address these issues, this study proposes a park space generative design system based on deep learning technology. This system generates design plans based on the topological relationships of landscape elements, then vectorizes the plan element information, and uses Grasshopper to generate three-dimensional models while synchronously fine-tuning parameters, rapidly completing the entire process from basic site conditions to model effect analysis. Experimental results show that: (1) the system, with the aid of AI-assisted technology, can rapidly generate space green space schemes that meet the designer's perspective based on site conditions; (2) this study has vectorized and three-dimensionalized various types of landscape design elements based on semantic information; (3) the analysis and visualization module constructed in this study can perform landscape analysis on the generated three-dimensional models and produce node effect diagrams, allowing users to modify the design in real time based on the effects, thus enhancing the system's interactivity.

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.

Taking on a historical lens, this paper traces the development of cybernetics and systems thinking back to the 1950s, when a group of interdisciplinary scholars converged to create a new theoretical model based on machines and systems for understanding matters of meaning, information, consciousness, and life. By presenting a genealogy of research in the landscape architecture discipline, the paper argues that landscape architects have been an important part of the development of cybernetics by materializing systems based on cybernetic principles in the environment through ecologically based landscape design. The landscape discipline has developed a design framework that provides transformative insights into understanding machine intelligence. The paper calls for a new paradigm of environmental engagement to understand matters of design and machine intelligence.

There have been theory-based endeavours that directly engage with AI and ML in the landscape discipline. By presenting a case that uses machine learning techniques to predict variables in a coastal environment, this paper provides empirical evidence of the forthcoming cybernetic environment, in which designers are conceptualized not as authors but as choreographers, catalyst agents, and conductors among many other intelligent agents. Drawing ideas from posthumanism, this paper argues that, to truly understand the cybernetic environment, we have to take on posthumanist ethics and overcome human exceptionalism.

In his new book, SuperSight: What Augmented Reality Means for Our Lives, Our Work, and the Way We Imagine the Future, author David Rose delves into the current state of the art of augmented reality, discussing how the technology is already transforming myriad industries -- from food service to medicine to education to construction and architecture -- and what it might accomplish in the near future. In the excerpt below, Rose takes a look at two companies leveraging computer vision and generative adversarial networks to reimagine existing properties as 21st century electrified smart homes. Excerpted with permission from SuperSight: What Augmented Reality Means for Our Lives, Our Work, and the Way We Imagine the Future by David Rose, published by BenBella Books. We should all be using solar panels. The average cost for a sustainable energy system has fallen about 70% in the last decade, from $5.86/watt to $1.50/ watt, so it's a financial no-brainer.