Felipe Moreno-Vera
Jorge Poco
ABSTRACT
In the last 40 years, Urban perception has become an important research area covering several fields, such as criminology, psychology, urban planning, Broken windows theory. It aims to analyze and interpret the behavior of the perception in cities. Urban perception focuses on understanding urban environments based on the characteristics of the city. With the rapidly increasing data availability and highly scalable data collection methods powered by modern web services, new techniques from other domains enabled the exploration of solutions to estimate urban perception (i.e., quantify urban perception autonomously). This work presents a methodology to explore the urban perception analysis task. The work relies on the benchmark dataset, Place Pulse. This dataset is used to perform our classification tasks concerning the category of safety in urban perception problems.
- Javier Rozo Alzate, Marta S Tabares, and Paola Vallejo. 2021. Graffiti and government in smart cities: a Deep Learning approach applied to Medellín City, Colombia. In International Conference on Data Science, E-learning and Information Systems 2021. 160–165.Google Scholar
Digital Library
- Sean M Arietta, Alexei A Efros, Ravi Ramamoorthi, and Maneesh Agrawala. 2014. City forensics: Using visual elements to predict non-visual city attributes. IEEE transactions on visualization and computer graphics 20, 12(2014), 2624–2633.Google Scholar
- Marco De Nadai, Jacopo Staiano, Roberto Larcher, Nicu Sebe, Daniele Quercia, and Bruno Lepri. 2016. The death and life of great Italian cities: a mobile phone data perspective. In Proceedings of the 25th international conference on world wide web. 413–423.Google ScholarDigital Library
- J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR09.Google Scholar
- Alexandre Magno Alves Diniz and Mark C Stafford. 2021. Graffiti and crime in Belo Horizonte, Brazil: The broken promises of broken windows theory. Applied Geography 131(2021), 102459.Google ScholarCross Ref
- Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, and Alexei Efros. 2012. What makes paris look like paris?(2012).Google Scholar
- Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell. 2014. Decaf: A deep convolutional activation feature for generic visual recognition. In International conference on machine learning. 647–655.Google ScholarDigital Library
- Abhimanyu Dubey, Nikhil Naik, Devi Parikh, Ramesh Raskar, and César A. Hidalgo. 2016. Deep Learning the City : Quantifying Urban Perception At A Global Scale. CoRR (2016).Google Scholar
- Kaiqun Fu, Zhiqian Chen, and Chang-Tien Lu. 2018. StreetNet: preference learning with convolutional neural network on urban crime perception. In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 269–278.Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), 770–778.Google Scholar
- Rachel Kaplan and Stephen Kaplan. 1989. The experience of nature: A psychological perspective. Cambridge university press.Google Scholar
- Kees Keizer, Siegwart Lindenberg, and Linda Steg. 2008. The Spreading of Disorder. Science (New York, N.Y.) 322 (12 2008), 1681–5. https://doi.org/10.1126/science.1161405Google Scholar
- Leonardo León-Vera and Felipe Moreno-Vera. 2018. Car Monitoring System in Apartments’ Garages by Small Autonomous Car Using Deep Learning. In Annual International Symposium on Information Management and Big Data. Springer, Springer International Publishing, 174–181.Google Scholar
- Xiaojiang Li, Chuanrong Zhang, and Weidong Li. 2015. Does the visibility of greenery increase perceived safety in urban areas? Evidence from the place pulse 1.0 dataset. ISPRS International Journal of Geo-Information 4, 3(2015), 1166–1183.Google ScholarCross Ref
- Xiaojiang Li, Chuanrong Zhang, Weidong Li, Robert Ricard, Qingyan Meng, and Weixing Zhang. 2015. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry & Urban Greening 14, 3 (2015), 675–685.Google ScholarCross Ref
- Min Lin, Qiang Chen, and Shuicheng Yan. 2013. Network in network. arXiv preprint arXiv:1312.4400(2013).Google Scholar
- Pall J Lindal and Terry Hartig. 2013. Architectural variation, building height, and the restorative quality of urban residential streetscapes. Journal of Environmental Psychology 33 (2013), 26–36.Google ScholarCross Ref
- Xiaobai Liu, Qi Chen, Lei Zhu, Yuanlu Xu, and Liang Lin. 2017. Place-centric visual urban perception with deep multi-instance regression. In Proceedings of the 25th ACM international conference on Multimedia. ACM, 19–27.Google ScholarDigital Library
- Kevin Lynch. 1984. Reconsidering the image of the city. In Cities of the Mind. Springer, 151–161.Google Scholar
- Weiqing Min, Shuhuan Mei, Linhu Liu, Yi Wang, and Shuqiang Jiang. 2019. Multi-Task Deep Relative Attribute Learning for Visual Urban Perception. IEEE Transactions on Image Processing 29 (2019), 657–669.Google ScholarDigital Library
- F. Moreno-Vera. 2019. Performing Deep Recurrent Double Q-Learning for Atari Games. In 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI). 1–4. https://doi.org/10.1109/LA-CCI47412.2019.9036763Google Scholar
- Felipe Moreno-Vera. 2021. Understanding Safety Based on Urban Perception. In International Conference on Intelligent Computing. Springer, 54–64.Google Scholar
- Felipe Moreno-Vera, Bahram Lavi, and Jorge Poco. 2021. Urban Perception: Can We Understand Why a Street Is Safe?. In Mexican International Conference on Artificial Intelligence. Springer, 277–288.Google ScholarDigital Library
- Nikhil Naik, Jade Philipoom, Ramesh Raskar, and Cesar Hidalgo. 2014. StreetScore: Predicting the Perceived safety of one million streetscapes.2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014).Google Scholar
- Nikhil Naik, Ramesh Raskar, and César A Hidalgo. 2016. Cities are physical too: Using computer vision to measure the quality and impact of urban appearance. American Economic Review 106, 5 (2016), 128–32.Google ScholarCross Ref
- Jack L Nasar. 1998. The evaluative image of the city. (1998).Google Scholar
- Vicente Ordonez and Tamara L. Berg. 2014. Learning High-level Judgments of Urban Perception. European Conference on Computer Vision (ECCV) (2014).Google Scholar
- Lorenzo Porzi, Samuel Rota Bulò, Bruno Lepri, and Elisa Ricci. 2015. Predicting and Understanding Urban Perception with Convolutional Neural Networks.Google Scholar
- Daniele Quercia, Neil Keith O’Hare, and Henriette Cramer. 2014. Aesthetic capital: what makes London look beautiful, quiet, and happy?. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing.Google ScholarDigital Library
- Mark Philip Salesses. 2012. Place Pulse: Measuring the collaborative image of the city. Ph.D. Dissertation. Massachusetts Institute of Technology.Google Scholar
- Robert J Sampson, Jeffrey D Morenoff, and Thomas Gannon-Rowley. 2002. Assessing “neighborhood effects”: Social processes and new directions in research. Annual review of sociology 28, 1 (2002), 443–478.Google Scholar
- Darshan Santani, Salvador Ruiz-Correa, and Daniel Gatica-Perez. 2018. Looking south: Learning urban perception in developing cities. ACM Transactions on Social Computing 1, 3 (2018), 1–23.Google ScholarDigital Library
- Herbert W Schroeder and Linda M Anderson. 1984. Perception of personal safety in urban recreation sites. Journal of leisure research 16, 2 (1984), 178–194.Google ScholarCross Ref
- Chanuki Illushka Seresinhe, Tobias Preis, and Helen Susannah Moat. 2017. Using deep learning to quantify the beauty of outdoor places. Royal Society open science 4, 7 (2017), 170170.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations (ICLR) (2014). arxiv:1409.1556 [cs.CV]Google Scholar
- Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, and Ryosuke Shibasaki. 2013. Modeling and probabilistic reasoning of population evacuation during large-scale disaster. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 1231–1239.Google ScholarDigital Library
- Eric K. Tokuda, Cláudio T. Silva, and Roberto Marcondes Cesar Jr.2019. Quantifying the presence of graffiti in urban environments. CoRR abs/1904.04336(2019). arxiv:1904.04336http://arxiv.org/abs/1904.04336Google Scholar
- Roger S Ulrich. 1979. Visual landscapes and psychological well-being. Landscape research 4, 1 (1979), 17–23.Google Scholar
- Matthias Wendt. 2009. The importance of death and life of great American cities (1961) by Jane Jacobs to the profession of urban planning. New Visions for Public Affairs 1 (2009), 1–24.Google Scholar
- James Q Wilson and George L Kelling. 1982. Broken windows. Atlantic monthly 249, 3 (1982), 29–38.Google Scholar
- Yongchao Xu, Qizheng Yang, Chaoran Cui, Cheng Shi, Guangle Song, Xiaohui Han, and Yilong Yin. 2019. Visual Urban Perception with Deep Semantic-Aware Network. In International Conference on Multimedia Modeling. Springer, 28–40.Google Scholar
- Fan Zhang, Bolei Zhou, Liu Liu, Yu Liu, Helene H Fung, Hui Lin, and Carlo Ratti. 2018. Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning 180 (2018), 148–160.Google ScholarCross Ref
Recommendations
Are Safer Looking Neighborhoods More Lively?: A Multimodal Investigation into Urban Life
MM '16: Proceedings of the 24th ACM international conference on MultimediaPolicy makers, urban planners, architects, sociologists, and economists are interested in creating urban areas that are both lively and safe. But are the safety and liveliness of neighborhoods independent characteristics? Or are they just two sides of ...
Predicting and Understanding Urban Perception with Convolutional Neural Networks
MM '15: Proceedings of the 23rd ACM international conference on MultimediaCities' visual appearance plays a central role in shaping human perception and response to the surrounding urban environment. For example, the visual qualities of urban spaces affect the psychological states of their inhabitants and can induce negative ...
Urban Perception: Can We Understand Why a Street Is Safe?
Advances in Computational IntelligenceAbstractThe importance of urban perception computing is relatively growing in machine learning, particularly in related areas to Urban Planning and Urban Computing. This field of study focuses on developing systems to analyze and map discriminant ...
Comments