| id |
caadria2023_167 |
| authors |
Sanatani, Rohit P. and Nagakura, Takehiko |
| year |
2023 |
| title |
The Many Faces of the Metropolis: Unsupervised Clustering of Urban Environments in Mumbai Based on Visual Features As Captured in City-Wide Street-View Imagery |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.555
|
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 555–564 |
| summary |
The larger visual identity of a city is often a blend of smaller and distinct visual character zones. Despite the recent popularity of street-view imagery for visual analytics, its role in uncovering such urban visual clusters has been fairly limited. Taking Mumbai as a demonstrative case, we present what is arguably the first city-wide visual cluster analysis of an Indian metropolis. We use a Dense Prediction Transformer (DPT) for semantic segmentation of over 28000 Google Street View (GSV) images collected from over 7000 locations across the city. Unsupervised k-means clustering is carried out on the extracted semantic features (such as greenery, sky-view, built-density etc.) for the identification of distinct urban visual typologies. Through iterative analysis, 7 key visual clusters are identified, and Principal Component Analysis (PCA) is used to visualize the variance across them. The feature distributions of each cluster are then qualitatively and quantitatively analysed in order to examine their unique visual configurations. Spatial distributions of the clusters are visualized as well, thus mapping out the different ‘faces’ of the city. It is hoped that the methodology outlined in this work serves as a base for similar cluster-based inquiries into the visual dimension of other cities across the globe. |
| keywords |
unsupervised clustering, urban environments, visual features, character zones, street-view imagery |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (3,872,936 bytes) |
| references |
Content-type: text/plain
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Biljecki, F., & Ito, K. (2021)
 Street View Imagery in Urban Analytics and Gis: a Review
, Landscape and Urban Planning, 215, 14217. Available at: https://doi.org/1.116/j.landurbplan.221.14217
|
|
|
|
|
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017)
Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs
, IEEE Transactions on Pattern Analysis and Machine Intelligence, 4(4), 834-848
|
|
|
|
|
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016)
The Cityscapes Dataset for Semantic Urban Scene Understanding
, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3213-3223
|
|
|
|
|
Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016)
Deep Learning the City: Quantifying Urban Perception At a Global Scale
, European Conference on Computer Vision, 196-212
|
|
|
|
|
Gorgul, E., Chen, C., Wu, K. K., & Guo, Y. (2019)
Measuring Street Enclosure and Its Influence to Human Physiology Through Wearable Sensors
, Adjunct Proceedings of the 219 ACM Int. Joint Conference on Pervasive and Ubiquitous Computing and Proceed. of the 219 ACM International Symposium on Wearable Computers (pp. 65-68)
|
|
|
|
|
Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., & Others. (2020)
The Open Images Dataset V4
, International Journal of Computer Vision, 128(7), 1956-1981
|
|
|
|
|
Naik, N., Philipoom, J., Raskar, R., & Hidalgo, C. (2014)
Streetscore-predicting the Perceived Safety of One Million Streetscapes
, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 779-785
|
|
|
|
|
Nasar, J. L. (1990)
The Evaluative Image of the City
, Journal of the American Planning Association, 56(1), 41-53
|
|
|
|
|
Ranftl, R., Bochkovskiy, A., & Koltun, V. (2021)
Vision Transformers for Dense Prediction
, Proceedings of the IEEE/CVF International Conference on Computer Vision, 12179-12188
|
|
|
|
|
Rogers, A. (1969)
Quadrat Analysis of Urban Dispersion: 1
, Theoretical techniques. Environment and Planning A, 1(1), 47-8. Available at: https://doi.org/1.168/a147
|
|
|
|
|
Sanatani, R., Nagakura, T., & Tsai, D. (2022)
The Tourists Image of the City: a Data-driven Comparative Analysis of the Features and Topics of Tourist Interest Across Three Global Cities
, Proceedins of the Iberoamerican Society of Digital Graphics, 222
|
|
|
|
|
Ye, Y., Zeng, W., Shen, Q., Zhang, X., & Lu, Y. (2019)
The Visual Quality of Streets: a Human-centred Continuous Measurement Based on Machine Learning Algorithms and Street View Images
, Environment and Planning B: Urban Analytics and City Science, 46(8), 1439-1457. http://dx.doi.org/1.1177/239988319828734
|
|
|
|
|
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018)
Measuring Human Perceptions of a Large-scale Urban Region Using Machine Learning
, Landscape and Urban Planning, 18, 148-16. Available at: https://doi.org/1.116/j.landurbplan.218.8.2
|
|
|
|
|
Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017)
Scene Parsing Through Ade20k Dataset
, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 633-641
|
|
|
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| last changed |
2023/06/15 23:14 |
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