# Geovisualization of Buildings: AI vs. Procedural Modeling
## 1 Introduction
Central to our inquiry are two overarching hypotheses, the first positing that despite the inherent heterogeneity of real-world buildings, procedural modeling methodologies should aptly accommodate their portrayal. The second hypothesis ventures into the exploration of various modalities through which this accommodation can be achieved [10; 11]: how to design a grammatical language that can be used to describe buildings with sets of procedural rules, which can then be transformed into 3D models. Such modality involves leveraging computer vision algorithms to discern fundamental building attributes from photographic inputs, thus laying the groundwork for machine learning-driven model generation. Although preliminary attempts at this approach have been made, their efficacy remains limited, necessitating further refinement and exploration [12; 13]. It explores the transformative impact of artificial intelligence on architectural heritage research, highlighting AI's capabilities in enhancing the documentation, preservation, and analysis of historical buildings. The chapter discusses innovative AI tools that significantly improve the accuracy and efficiency of heritage conservation efforts [14]. Some authors explore the intersection of AI and design. It discusses how AI techniques are applied in architectural design, geographic visualization, and program modeling. The text emphasizes AI's role in enhancing creativity and efficiency within these fields [15]. Authors have been exploring innovative approaches to urban planning. This focuses on developing sustainable urban spatial structures using advanced modeling techniques, emphasizing environmental impact reduction and improving urban living conditions. This study highlights the importance of integrating ecological considerations in city planning for future resilience [16]. Conversely, our second approach, which serves as the focal point of this paper, entails harnessing the capabilities of artificial intelligence (AI) [17; 18]. Herein, detailed descriptions of buildings, encompassing all requisite specifications, serve as inputs to AI algorithms, which subsequently generate scripts tailored to each individual building. This methodology, referred to as reverse procedural modeling, represents a novel synthesis of AI-driven automation with procedural modeling paradigms. To validate the viability and effectiveness of the proposed methodology, a comparative analysis is conducted, comparing AI-generated building models with manually created models using traditional procedural modeling techniques. By identifying discrepancies and assessing the accuracy of AI-generated models in relation to their manually crafted counterparts, the aim is to provide empirical evidence supporting the efficacy and utility of the proposed approach [19; 20]. Procedural building modeling, in conjunction with advancements in artificial intelligence (AI) and access to online databases, opens the door to revolutionary methods of creating complex structures like buildings. This fusion of technologies enables the automation of modeling processes, reducing the need for manual interventions, and accelerating the entire procedure. The ways in which automatic procedural building modeling can be executed using building descriptions or visual recognition of building characteristics, AI, online databases, and machine learning will be explored in detail [21; 22]. Automated procedural building modeling integrates various cutting-edge technologies and methodologies to streamline the architectural design process. It begins with the extraction of building descriptions or the recognition of building characteristics from textual or visual inputs. Natural language processing (NLP) techniques are employed to parse textual descriptions, while computer vision algorithms analyze images or videos to identify architectural features [23; 24]. Once the initial input is obtained, artificial intelligence (AI) algorithms come into play to analyze and interpret the data.