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Building block level urban land-use information retrieval based on Google Street View images

Xiaojiang Li, Chuanrong Zhang, Weidong Li
2017 GIScience & Remote Sensing  
In this study, we propose a new method to derive land use information at building block level based on machine learning and geo-tagged street-level imagery -Google Street View images.  ...  Machine learning is further used to categorize different images based on the calculated image features of different street-level images.  ...  Based on the panorama ID information, we can download GSV images for different heading angles using Google Street View static Image API.  ... 
doi:10.1080/15481603.2017.1338389 fatcat:ewrffnlzpfdqbioggi7wkmyd7u

Building instance classification using street view images

Jian Kang, Marco Körner, Yuanyuan Wang, Hannes Taubenböck, Xiao Xiang Zhu
2018 ISPRS journal of photogrammetry and remote sensing (Print)  
Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades.  ...  The proposed method is based on Convolutional Neural Networks (CNNs) which classify facade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually  ...  Besides, compared to the roof structures, the information of façade structures displayed in street view images is richer and more sufficient to be used for building instance classification. street view  ... 
doi:10.1016/j.isprsjprs.2018.02.006 fatcat:cvqzdpuznbaudcxem6m67lbi64

Estimating Building Age from Google Street View Images Using Deep Learning (Short Paper)

Yan Li, Yiqun Chen, Abbas Rajabifard, Kourosh Khoshelham, Mitko Aleksandrov, Michael Wagner
2018 International Conference Geographic Information Science  
With a large volume of building images being accessible online via API (such as Google Street View), as well as the fast development of image processing techniques such as deep learning, it becomes feasible  ...  Second, an image-base building age estimation framework is proposed which doesn't require information on building height, floor area, construction materials and therefore makes the analysis process simpler  ...  Google Street View images download Using the Google Street View Image API, we directly submit a list of addresses, for example, "172 Bouverie St, Carlton VIC 3053", and then store the retrieved house images  ... 
doi:10.4230/lipics.giscience.2018.40 dblp:conf/giscience/LiCRKA18 fatcat:6unsfvabqjbwjpmif4kg3fi46y

Urban neighbourhood environment assessment based on street view image processing: A review of research trends

Nan He, Guanghao Li
2021 Environmental Challenges  
This paper systematically reviews the research trends of existing publications on the use of street view images for the quantitative analysis of urban neighbourhood environments.  ...  Street view image processing can be used to obtain spatial elements of large scale urban neighborhoods, thus enabling rapid urban neighbourhood evaluation.  ...  The next most cited papers are in the areas of land use classification, building instance classification and urban environment.  ... 
doi:10.1016/j.envc.2021.100090 fatcat:6buqaffdwvgojlkkhs3wtj2w6i

Toward Seamless Multiview Scene Analysis From Satellite to Street Level

Sebastien Lefevre, Devis Tuia, Jan Dirk Wegner, Timothee Produit, Ahmed Samy Nassar
2017 Proceedings of the IEEE  
Numerous works exist that merge information from remote sensing and images acquired from the ground for tasks like land cover mapping, object detection, or scene understanding.  ...  We review recent works that attempt to combine images taken from the ground and overhead views for purposes like scene registration, reconstruction, or classification.  ...  from Google Street View panoramic images.  ... 
doi:10.1109/jproc.2017.2684300 fatcat:r3hyfdtjzzaknl25cpygxrcgq4


P. Tutzauer, N. Haala
2017 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
We aim to extract this city-relevant semantic information automatically from Street View (SV) imagery.  ...  We present a first step in bridging this gap by proposing a pipeline to use crawled urban imagery and link it with ground truth cadastral data as an input for automatic building use classification.  ...  ACKNOWLEDGEMENTS We would like to thank the German Research Foundation (DFG) for financial support within the project D01 of SFB/Transregio 161.  ... 
doi:10.5194/isprs-archives-xlii-1-w1-143-2017 fatcat:viaxcv7jqvabljcpiaakuyk5dm

Remote Sensing of Urban Forests

Giovanni Sanesi, Vincenzo Giannico, Mario Elia, Raffaele Lafortezza
2019 Remote Sensing  
This Special Issue hosts papers focusing on the temporal and spatial dynamics of urban forests with special attention given to the most recent remote sensing technologies as well as advanced methods for  ...  processing geospatial data and extracting meaningful information.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11202383 fatcat:w4jp25qymnchrc2xfch3gneumm

Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning

Chuan-Bo Hu, Fan Zhang, Fang-Ying Gong, Carlo Ratti, Xin Li
2019 Building and Environment  
To overcome these difficulties, we develop a street canyon classification approach using publicly available Google Street View (GSV) images.  ...  Urban canyon classification plays an important role in analyzing the impact of urban canyon geometry on urban morphology and microclimates.  ...  For example, Google Street View (GSV) images have been widely used in quantitative research of built-environment and urban landscapes. GSV images cover 39 countries and approximately 3000 cities.  ... 
doi:10.1016/j.buildenv.2019.106424 fatcat:u7jow2cxu5h2jn6qurezb6ttye

Model Fusion for Building Type Classification from Aerial and Street View Images

Eike Jens Hoffmann, Yuanyuan Wang, Martin Werner, Jian Kang, Xiao Xiang Zhu
2019 Remote Sensing  
This article addresses the question of mapping building functions jointly using both aerial and street view images via deep learning techniques.  ...  In this way, the significant differences in appearance of aerial and street view images are taken into account.  ...  Abbreviations The following abbreviations are used in this manuscript: API Application programming interface CNN Convolutional Neural Network OSM OpenStreetMap SVM Support Vector Machine CVUSA Cross-View  ... 
doi:10.3390/rs11111259 fatcat:k443dywpcngnnbkpwh272cqg5q

Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions

Zhaoya Gong, Qiwei Ma, Changcheng Kan, Qianyun Qi
2019 Sustainability  
Streets, as one type of land use, are generally treated as developed or impervious areas in most of the land-use/land-cover studies.  ...  This paper aims to characterize and classify street spaces based on features extracted from street view images by a deep learning model of computer vision.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su11226424 fatcat:pof3ymukyvhj5nesovvgsvgwve

Residential land extraction from high spatial resolution optical images using multifeature hierarchical method

Zhongliang Fu, Xiaoli Liang
2019 Journal of Applied Remote Sensing  
aerial images, even in the pixel-level LULC mapping. 52 Thus Google Earth images can also be used as aerial images for scene classification.  ...  First, RL is extracted based on the gray level concurrence matrix and a fuzzy classification algorithm.  ...  Acknowledgments This paper was substantially supported by the National Key R&D Program of China (Grant No. 2017YFB0503004) and the National Natural Science Foundation of China (Project Nos. 41301525 and  ... 
doi:10.1117/1.jrs.13.026515 fatcat:sieu5bd3svb63gwz2pwt66zdd4

Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images [article]

Yi Zhu and Xueqing Deng and Shawn Newsam
2018 arXiv   pre-print
Individual images are classified using a novel convolutional neural network containing two streams, one for recognizing objects and another for recognizing scenes.  ...  We develop a general framework that uses Flickr images to map 45 different land-use classes for the City of San Francisco.  ...  The image-level classification accuracy is computed based on the keyword labels, while the shapefile-level land use mapping accuracy is calculated based on the proposed ground truth land use map of San  ... 
arXiv:1802.02668v1 fatcat:u537amzftrc4vh4j24vwhvvgjm

On the location dependence of convolutional neural network features

Scott Workman, Nathan Jacobs
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We present results on multiple datasets, including a new dataset we introduce containing hundreds of thousands of ground-level and aerial images in a large region centered around San Francisco.  ...  we achieve state-of-the-art performance on a benchmark dataset).  ...  Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied  ... 
doi:10.1109/cvprw.2015.7301385 dblp:conf/cvpr/WorkmanJ15 fatcat:w2etnkztqzhvvn72steu3bekky

Identifying patterns in urban housing density in developing countries using convolutional networks and satellite imagery

Rahman Sanya, Ernest Mwebaze
2020 Heliyon  
The use of Deep Neural Networks for remote sensing scene image analysis is growing fast.  ...  Our results for scene image classification are comparable to current state-of-the-art, despite focusing only on most difficult classes in those works.  ...  ., 2018) proposed a framework for classifying images of individual buildings based on functionality by utilizing CNN and street view images combined with remote sensing imagery.  ... 
doi:10.1016/j.heliyon.2020.e05617 pmid:33319091 pmcid:PMC7725730 fatcat:bs5tkcu3azevjjdgiwkducvyc4

Street View Imagery (SVI) in the Built Environment: A Theoretical and Systematic Review

Yongchang Li, Li Peng, Chengwei Wu, Jiazhen Zhang
2022 Buildings  
Street view imagery (SVI) provides efficient access to data that can be used to research spatial quality at the human scale.  ...  A notable trend is the application of SVI towards a focus on the perceptions of the built environment, which provides a more refined and effective way to depict urban forms in terms of physical and social  ...  Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/buildings12081167 fatcat:7sdadsypufbktpyqanhc2allrm
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