The effectiveness of machine learning(ML)models for architectural applications relies on high-quality datasets balanced with advancements in model architecture and computational capacity.Current methods for evaluating...The effectiveness of machine learning(ML)models for architectural applications relies on high-quality datasets balanced with advancements in model architecture and computational capacity.Current methods for evaluating architectural floor plan datasets typically depend on explicit semantic annotations,which limit their effectiveness and scalability when annotations are unavailable or inconsistent.To address this limitation,this research develops an isovist-based latent representation approach to quantitatively measure typicality and diversity within architectural datasets without relying on semantic labels.We introduce Isovist Latent Norm Typicality,a metric that leverages the statistical structure of latent representations derived from isovist morphological features using a variational autoencoder(VAE).This metric quantifies typicality by analyzing distributional shifts in latent representations between individual floor plans and a reference dataset using a modified Wasserstein distance.Experimental results demonstrate the approach’s ability to distinguish typical from atypical floor plan configurations,capturing the morphological features that complement conventional metrics.Comparative analysis indicates that our method provides insights into spatial organization,correlating with conventional properties such as programmatic diversity and spatial openness.By quantifying typicality through purely morphological features,the proposed methodology facilitates dataset curation prior to costly semantic annotation,enhancing dataset quality and enabling scalability to more extensive and diverse architectural datasets.展开更多
Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threat...Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.展开更多
文摘The effectiveness of machine learning(ML)models for architectural applications relies on high-quality datasets balanced with advancements in model architecture and computational capacity.Current methods for evaluating architectural floor plan datasets typically depend on explicit semantic annotations,which limit their effectiveness and scalability when annotations are unavailable or inconsistent.To address this limitation,this research develops an isovist-based latent representation approach to quantitatively measure typicality and diversity within architectural datasets without relying on semantic labels.We introduce Isovist Latent Norm Typicality,a metric that leverages the statistical structure of latent representations derived from isovist morphological features using a variational autoencoder(VAE).This metric quantifies typicality by analyzing distributional shifts in latent representations between individual floor plans and a reference dataset using a modified Wasserstein distance.Experimental results demonstrate the approach’s ability to distinguish typical from atypical floor plan configurations,capturing the morphological features that complement conventional metrics.Comparative analysis indicates that our method provides insights into spatial organization,correlating with conventional properties such as programmatic diversity and spatial openness.By quantifying typicality through purely morphological features,the proposed methodology facilitates dataset curation prior to costly semantic annotation,enhancing dataset quality and enabling scalability to more extensive and diverse architectural datasets.
基金supported by the Ministry of Industry and Information Technology(No.23100002022102001)。
文摘Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors,such as static and dynamic obstructions as well as risks of exposure to enemy detection,which threaten flight safety and mission success.Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths,without accounting for constraints imposed by enemy detection and strike capabilities.Such a simplified approach can yield safety-compromising routes in highly complex urban airspace.To address these limitations,this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs,which integrates UAV performance constraints,environmental limitations,and exposure risks.An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length.In the case study set in a typical mixed-use urban area,analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments.Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index,which represents the likelihood of enemy detection,and the path length,which corresponds to mission execution time.
