With Focus on Automated Labelling and Dataset Acquisition

Recently, Deep Learning (DL) has been widely used in the automation of urban layout processes. This study proposes a rule-based and Generative Adversarial Network (GAN) workflow to automatically select and label urban datasets to train customized GAN models for the generation of urban layout proposals. The developed workflow automatically collects and labels urban typology samples from the open-source map.  Furthermore, it controls the results of the GAN process through labeling and provides real-time urban layout suggestions in a co-design manner. The conducted case study shows that the average value of the GAN results, trained from an automatically generated dataset, meets the site's requirements. The developed co-design strategy allows the architect to control the GAN process to perform iterations of urban layout tasks. The research contributes to the found research gap in GAN applications in the field of urban design and plan

This map is generated in order to reveal the intersection and development of various research fields of computer science. Through analyzing the citing, cited, co-citing and co-cited relationships, t

Keywords: Academic Map, Subjects, Research Fields, Research Topics

According to all the topics and their children topics listed by Microsoft Academic, this Academic Map are generated. The data

Keywords: layout algorithm, circle-packing

Background:

The circle-pack layout represents nodes in a hierarchal model as circles and positions each child node’s circle inside its parent’s c

Keywords: Pseudo base station, visual analysis, interactive exploratory, decision-making support

The Pseudo base stations cause a great number of social and economic security problems with spreading spam messages. However, it is a serious challenge for security department to recognize the behav

The space layout of a reasonable modular building prototype is a time[1]consuming and complex process. Many studies have achieved the optimization of automatic spatial layouts based on spatial adjacency simulation. Although machine produced plans satisfy the adjacency and area constraints, people still need further manual modifications to meet other spatially complex design requirements. Motivated by this, we are trying to provide a human-machine collaborative design workflow that simulates the spatial adjacency relationship based on physical models. Compared with previous works, our workflow enhances the automated space layout process by allowing designers to use environment anchors making decisions in automatic layout iterations. A case study is proposed to demonstrate that the solution generated by workflow can initially complete different customized design tasks. The workflow combines the advantages of the designer's decision-making experience in manual modelling with the machine's