Build a Google Street View

2017 ◽  
Vol 15 (8) ◽  
pp. 6-6 ◽  
Keyword(s):  
Google Street View ◽  
Street View

scholarly journals Domain Adaptation Deep Attention Network for Automatic Logo Detection and Recognition in Google Street View

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ervin Yohannes ◽  
Chih-Yang Lin ◽  
Timothy K. Shih ◽  
Chen-Ya Hong ◽  
Avirmed Enkhbat ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
Attention Network ◽  
Google Street View ◽  
Street View ◽  
Detection And Recognition

Adaptation and testing of a microscale audit tool to assess liveability using google street view: MAPS-liveability

2021 ◽  
Vol 22 ◽  
pp. 101226
Author(s):  
Claire L. Cleland ◽  
Sara Ferguson ◽  
Frank Kee ◽  
Paul Kelly ◽  
Andrew James Williams ◽  
...  
Keyword(s):  
Audit Tool ◽  
Google Street View ◽  
Street View

scholarly journals STATISTICAL ANALYSIS VIA GOOGLE STREET VIEW OF SHOP FACADE DESIGN

2012 ◽  
Vol 18 (39) ◽  
pp. 693-698
Author(s):  
Eisuke TABATA ◽  
Kazemitsu FUKAMATSU ◽  
Kazuhisa TSUNEKAWA ◽  
Gen TANIGUCHI
Keyword(s):  
Statistical Analysis ◽  
Google Street View ◽  
Street View

Something that Disturbs: Encounters between Animals and Optical Machines

2020 ◽  
Vol 11 (2020) ◽  
Author(s):  
Pauline Chasseray-Peraldi
Keyword(s):  
Digital Images ◽  
Google Street View ◽  
Media Technologies ◽  
Street View ◽  
Semiotic Approach ◽  
The Web

Images of encounters between animals and drones or Google Street View cars are quite viral on the web. This article focuses on the different regimes of animacy and conflicts of affects in these images using an anthropo- semiotic approach. It investigates how other- ness reveals something that exceeds us, from the materiality of the machine to systems of values. It suggests that the disturbance of ani- mal presence in contemporary digital images helps us to read media technologies.

scholarly journals Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

2018 ◽  
Vol 52 (21) ◽  
pp. 12563-12572 ◽  
Author(s):  
Kyle P. Messier ◽  
Sarah E. Chambliss ◽  
Shahzad Gani ◽  
Ramon Alvarez ◽  
Michael Brauer ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Mobile Monitoring ◽  
Land Use Regression ◽  
Google Street View ◽  
Street View

scholarly journals Alcohol in urban streetscapes: a comparison of the use of Google Street View and on-street observation

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Chris Clews ◽  
Roza Brajkovich-Payne ◽  
Emily Dwight ◽  
Ayob Ahmad Fauzul ◽  
Madeleine Burton ◽  
...  
Keyword(s):  
Google Street View ◽  
Street View

scholarly journals Application of Method for Calculating Sky View Factor Using Google Street View: Relation Between Sky View Factor and Physical Elements in Urban Space

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Shoko Nishio ◽  
Fumiko Ito
Keyword(s):  
Urban Space ◽  
Computation Method ◽  
View Factor ◽  
Statistical Process ◽  
Google Street View ◽  
Sky View Factor ◽  
Street View ◽  
The Difference ◽  
Physical Elements

<p><strong>Abstract.</strong> We applied a computation method of calculating the sky view factor (SVF) using Google Street View to Shibuya area, Tokyo, for the purpose of examining the relation between the SVF/SVF change and physical elements. The distribution of the SVF calculated by the above method was visualized, and the statistical process showed the tendency of a high SVF in quasi-residential districts and roadsides of high-graded trunk roads. The difference in the SVF change was small at 10-m intervals. The SVF change tended to be more apparent near an intersection and at different elevations.</p>

scholarly journals 3D MAP SYSTEM FOR TREE MONITORING IN HONG KONG USING GOOGLE STREET VIEW IMAGERY AND DEEP LEARNING

2020 ◽  
Vol V-3-2020 ◽  
pp. 765-772
Author(s):  
M. Li ◽  
W. Yao
Keyword(s):  
Hong Kong ◽  
Public Awareness ◽  
Urban Trees ◽  
Depth Image ◽  
Accurate Information ◽  
Ground Truth Data ◽  
Google Street View ◽  
Urban Tree ◽  
Tree Detection ◽  
Street View

Abstract. In densely built urban areas such as Hong Kong, the positive effect of urban trees is to help maintain high environmental and social sustainability for the city while unmanaged trees lead to negative effects such as accidents, outbreaks of pests and diseases. The public awareness of urban tree population has been increasing and preserving all the benefits offered by trees, a continuous monitoring concept would be required. In this work, an efficient 3D map system for tree inventory in Hong Kong is presented to the based on automated tree detection from publicly available Google street view (GSV) panorama images. First, Convolutional Neural Networks (CNNs) based object detector and classifier – YOLOv3 with pretrained model is adopted to learn GSV images to detect tree objects. GSV depth image has been utilized to decode depth values of each GSV panorama image and will provide accurate information to calculate the tree geographic position. A “field of view” filter was designed to remove duplicated tree detection within the overlapped areas followed by spatial clustering applied to further increase the tree localization accuracy. The average distance between the detected trees and ground truth data was achieved within 3 meters for selected roads used for the experiment. Second, a 3D Map platform prototype for facilitating the urban tree monitoring and management was developed. Currently, there is no true 3D platform for interpreting the results of tree records in Hong Kong city areas. With the help of webGL technology, contemporary browsers are able to show 3D buildings, terrain and other scene components together with the obtained tree records in an open source 3D GIS platform, the level of visualization is enhanced as all the detected trees are placed on the 3D digital terrain model. Consequently, it is easy for end-users to know the actual position of the trees and their distribution.

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