Redirigiendo al acceso original de articulo en 18 segundos...
ARTÍCULO
TITULO

A Complete Reinforcement-Learning-Based Framework for Urban-Safety Perception

Yaxuan Wang    
Zhixin Zeng    
Qiushan Li and Yingrui Deng    

Resumen

Urban-safety perception is crucial for urban planning and pedestrian street preference studies. With the development of deep learning and the availability of high-resolution street images, the use of artificial intelligence methods to deal with urban-safety perception has been considered adequate by many researchers. However, most current methods are based on the feature-extraction capability of convolutional neural networks (CNNs) with large-scale annotated data for training, mainly aimed at providing a regression or classification model. There remains a lack of interpretable and complete evaluation systems for urban-safety perception. To improve the interpretability of evaluation models and achieve human-like safety perception, we proposed a complete decision-making framework based on reinforcement learning (RL). We developed a novel feature-extraction module, a scalable visual computational model based on visual semantic and functional features that could fully exploit the knowledge of domain experts. Furthermore, we designed the RL module?comprising a combination of a Markov decision process (MDP)-based street-view observation environment and an intelligent agent trained using a deep reinforcement-learning (DRL) algorithm?to achieve human-level perception abilities. Experimental results using our crowdsourced dataset showed that the framework achieved satisfactory prediction performance and excellent visual interpretability.

 Artículos similares

       
 
Yuzhe Liu, Libo Chen, Yusu Xu and Jingqiu Yang    
Contemporary studies largely concentrate on the physical aspects of architecture, yet within the sphere of design, the gap between user experience and the designer?s intention is an undeniable fact. This gap, illustrating the contrast between the spatial... ver más
Revista: Buildings

 
Bridget Thodesen, Erlend Andenæs, Rolf André Bohne and Tore Kvande    
The timely implementation of climate adaptation measures for the urban environment is essential to the creation of robust cities. Within Norway, these adaptation measures are undertaken at the municipal level. Unfortunately, the implementation of adaptat... ver más
Revista: Urban Science

 
Kuntao Hu, Ziqi Xu, Xiufang Wang, Yingyu Wang, Haoran Li and Yibing Zhang    
The color of urban streets plays a crucial role in shaping a city?s image, enhancing street appeal, and optimizing the experience of citizens. Nevertheless, the relationship between street color environment and residents? perceptions has rarely been deep... ver más
Revista: Buildings

 
Yongchao Song, Tao Huang, Xin Fu, Yahong Jiang, Jindong Xu, Jindong Zhao, Weiqing Yan and Xuan Wang    
Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universa... ver más

 
Jing Ye, Lingyan Chen and Yushan Zheng    
The acoustic environment can influence people?s perceptions and experiences and shape the soundscape. The soundscape has a unique role in shaping the cultural identity of a regional culture. Artificial sounds are an essential source of sounds in historic... ver más
Revista: Buildings