A Data-Driven Framework for Transferring the Ground-View Image into the Aerial-View Scene
Authors: 
Wei Hu, Wei Li, Yongzhen Huang, Guowei Liu, Duodong WuAuthors Info & Claims
Wei Hu, Wei Li, Yongzhen Huang, Guowei Liu, Duodong WuAuthors Info & ClaimsPages 147 - 153
References
[1]
Y. Li, L. Guo, J. Rao, L. Xu, S. Jin, “Road segmentation based on hybrid convolutional network for high-resolution visible remote sensing image,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 4, pp. 613-617, 2018.
[2]
Q. Shi, X. Liu, X. Li, “Road detection from remote sensing images by generative adversarial networks,” IEEE access, vol. 6, pp. 25486-25494, 2017.
[3]
N. More, V. Nikam, B. Banerjee, “Machine learning on high performance computing for urban greenspace change detection: satellite image data fusion approach,” International Journal of Image and Data Fusion, vol. 11, no. 3, pp. 218-232, 2020.
[4]
F. Sedano, P. Kempeneers, P. Strobl, J. Kucera, P. Vogt, L. Seebach, J. San-Miguel-Ayanz, “A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 5, pp. 588-596, 2011.
[5]
R. Cao, J. Zhu, W. Tu, Q. Li, J. Cao, B. Liu, Q. Zhang, G. Qiu, “Integrating aerial and street view images for urban land use classification,” Remote Sensing, vol. 10, no. 10, pp. 1553, 2018.
[6]
C. Tao, J. Qi, Y. Li, H. Wang, H. Li, “Spatial information inference net: Road extraction using road-specific contextual information,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 158, pp. 155-166, 2019.
[7]
A. G. Rundle, M. D. Bader, C. A. Richards, K. M. Neckerman, J. O. Teitler, “Using Google Street View to audit neighborhood environments,” American journal of preventive medicine, vol. 40, no. 1, pp. 94-100, 2011.
[8]
F. Zhang, D. Zhang, Y. Liu, H. Lin, “Representing place locales using scene elements,” Computers, Environment and Urban Systems, vol. 71, pp. 153-164, 2018.
[9]
J. Xiao, L. Quan. "Multiple view semantic segmentation for street view images". IEEE, in 2009 IEEE 12th international conference on computer vision. 2009.
[10]
Y. Ye, W. Zeng, Q. Shen, X. Zhang, Y. Lu, “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, vol. 46, no. 8, pp. 1439-1457, 2019.
[11]
L. Chen, Y. Lu, Q. Sheng, Y. Ye, R. Wang, Y. Liu, “Estimating pedestrian volume using Street View images: A large-scale validation test,” Computers, Environment and Urban Systems, vol. 81, pp. 101481, 2020.
[12]
A. Campbell, A. Both, Q. C. Sun, “Detecting and mapping traffic signs from Google Street View images using deep learning and GIS,” Computers, Environment and Urban Systems, vol. 77, pp. 101350, 2019.
[13]
E. L. Glaeser, S. D. Kominers, M. Luca, N. Naik, “Big data and big cities: The promises and limitations of improved measures of urban life,” Economic Inquiry, vol. 56, no. 1, pp. 114-137, 2018.
[14]
C. Wu, N. Peng, X. Ma, S. Li, J. Rao, “Assessing multiscale visual appearance characteristics of neighbourhoods using geographically weighted principal component analysis in Shenzhen, China,” Computers, Environment and Urban Systems, vol. 84, pp. 101547, 2020.
[15]
J. Liang, J. Gong, J. Zhang, Y. Li, D. Wu, G. Zhang, “GSV2SVF-an interactive GIS tool for sky, tree and building view factor estimation from street view photographs,” Building and Environment, vol. 168, pp. 106475, 2020.
[16]
R. Wang, Y. Lu, J. Zhang, P. Liu, Y. Yao, Y. Liu, “The relationship between visual enclosure for neighbourhood street walkability and elders’ mental health in China: using street view images,” Journal of Transport & Health, vol. 13, pp. 90-102, 2019.
[17]
D. Ki, S. Lee, “Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning,” Landscape and Urban Planning, vol. 205, pp. 103920, 2021.
[18]
S. Zhou, D. Nie, E. Adeli, J. Yin, J. Lian, D. Shen, “High-resolution encoder–decoder networks for low-contrast medical image segmentation,” IEEE Transactions on Image Processing, vol. 29, pp. 461-475, 2019.
[19]
V. Badrinarayanan, A. Kendall, R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 12, pp. 2481-2495, 2017.
[20]
H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia. “Pyramid scene parsing network”. in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017.
[21]
R. L. Cook, T. Porter, L. Carpenter. “Distributed ray tracing”. in Proceedings of the 11th annual conference on Computer graphics and interactive techniques, 1984.
[22]
B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, A. Torralba, “Semantic understanding of scenes through the ade20k dataset,” International Journal of Computer Vision, vol. 127, no. 3, pp. 302-321, 2019.
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October 2021
3136 pages
ISBN:9781450385046
DOI:10.1145/3495018
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Published: 14 March 2022
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AIAM2021
AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture
October 23 - 25, 2021
Manchester, United Kingdom
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