Urban Envelope: Representing Audio and Visual Features Extracted from Deep Learning Techniques-Reference-Cited by-同舟云学术

Urban Envelope: Representing Audio and Visual Features Extracted from Deep Learning Techniques

Published:2021 Issue: Volume: Page:231-239
ISSN:2366-2557
Container-title:Lecture Notes in Civil Engineering
language:
Short-container-title:

Author:

Verma Deepank^ORCID,Jana Arnab^ORCID

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-33-4114-2_18

Reference18 articles.

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2. Nasar, J. L. (1989). Perception, cognition, and evaluation of urban places. In I. Altman & E. H. Zube (Eds.), Public places and spaces (pp. 31–56). Boston, MA: Springer US. https://doi.org/10.1007/978-1-4684-5601-1_3.

3. Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016). Deep learning the city: Quantifying urban perception at a global scale. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS (Vol. 9905, pp. 196–212). https://arxiv.org/abs/1608.01769.

4. Larkin, A., & Hystad, P. (2018). Evaluating street view exposure measures of visible green space for health research. Journal of Exposure Science and Environmental Epidemiology, 1–10. https://doi.org/10.1038/s41370-018-0017-1.

5. Li, X., & Ratti, C. (2018). Mapping the spatial distribution of shade provision of street trees in Boston using Google street view panoramas. Urban Forestry & Urban Greening, 31, 109–119. https://doi.org/10.1016/j.ufug.2018.02.013.