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Review Article

Urban land-use analysis using proximate sensing imagery: a survey

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Pages 2129-2148 | Received 30 Nov 2020, Accepted 16 Apr 2021, Published online: 03 May 2021

References

  • Antoniou, V., et al. 2016. Investigating the feasibility of geo-tagged photographs as sources of land cover input data. ISPRS International Journal of Geo-Information, 5 (5), 64. doi:https://doi.org/10.3390/ijgi5050064
  • Arsanjani, J.J., et al. 2013. Toward mapping land-use patterns from volunteered geographic information. International Journal of Geographical Information Science, 27 (12), 2264–2278. doi:https://doi.org/10.1080/13658816.2013.800871
  • Arsanjani, J.J., et al., 2015. 2. Quality assessment of the contributed land use information from OpenStreetMap versus authoritative datasets. Cham: Springer, 37–58.
  • ATTOM Data Solutions, 2020. Points of interest data [online]. Available from: https://www.attomdata.com/data/neighborhood-data/points-interest-data/ [Accessed Nov 2020]
  • Avila, S., et al., 2011. Bossa: extended bow formalism for image classification. In: 18th IEEE International Conference on Image Processing. Athens, Greece, 2909–2912.
  • Badrinarayanan, V., Kendall, A., and Cipolla, R., 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (12), 2481–2495. doi:https://doi.org/10.1109/TPAMI.2016.2644615
  • Bansal, A., et al., 2017. Pixelnet: representation of the pixels, by the pixels, and for the pixels. arXiv preprint arXiv:1702.06506.
  • Bromley, J., et al., 1994. Signature verification using a “Siamese” time delay neural network. In: Advances in Neural Information Processing Systems. Denver, USA, 737–744.
  • Cai, Z. and Vasconcelos, N., 2018. Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 6154–6162.
  • Cao, R., et al. 2018. Integrating aerial and street view images for urban land use classification. Remote Sensing, 10 (10), 1553. doi:https://doi.org/10.3390/rs10101553
  • Cao, R. and Qiu, G., 2018. Urban land use classification based on aerial and ground images. In: International Conference on Content-Based Multimedia Indexing. La Rochelle, France, 1–6.
  • Chang, S., et al. 2020. Mapping the essential urban land use in Changchun by applying random forest and multi-source geospatial data. Remote Sensing, 12 (15), 2488. doi:https://doi.org/10.3390/rs12152488
  • Chen, L.C., et al., 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on computer Vision. Munich, Germany, 801–818.
  • Copernicus Land Monitoring Service, 2020. Urban Atlas. Available from: https://land.copernicus.eu/local/urban-atlas. [Accessed Nov 2020].
  • Cordts, M., et al., 2016. The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 3213–3223.
  • Defazio, A., Bach, F.R., and Lacoste-Julien, S., 2014. SAGA: a fast incremental gradient method with support for non-strongly convex composite objectives. CoRR, abs/1407.0202.
  • Deng, J., et al., 2009. Imagenet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 248–255.
  • Dustin, S., 2020. Social media 2020: top networks by the numbers [online]. Available from: https://dustinstout.com/social-media-statistics/ [Accessed Nov 2020].
  • Estima, J. and Painho, M., 2013. Exploratory analysis of OpenStreetMap for land use classification. In: Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information. Orlando, Florida, USA, 39–46.
  • Estima, J. and Painho, M., 2015. 13. Investigating the potential of OpenStreetMap for land use/land cover production: a case study for continental Portugal. Springer, Cham, 273–293. OpenStreetMap in GIScience.
  • Fan, H., Zipf, A., and Fu, Q., 2014. 2. Estimation of building types on OpenStreetMap based on urban morphology analysis. Springer, Cham, 19–35.
  • Fang, F., et al. 2018. Urban land-use classification from photographs. IEEE Geoscience and Remote Sensing Letters, 15 (12), 1927–1931. doi:https://doi.org/10.1109/LGRS.2018.2864282
  • Feng, T., et al., 2018. Urban zoning using higher-order markov random fields on multiview imagery data. In: Proceedings of the European Conference on Computer Vision. Munich, Germany, 614–630.
  • Fonte, C.C. and Martinho, N., 2017. Assessing the applicability of OpenStreetMap data to assist the validation of land use/land cover maps. International Journal of Geographical Information Science, 31 (12), 2382–2400. doi:https://doi.org/10.1080/13658816.2017.1358814
  • Gao, S., Janowicz, K., and Couclelis, H., 2017. Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS, 21 (3), 446–467. doi:https://doi.org/10.1111/tgis.12289
  • Haklay, M. and Weber, P., 2008. OpenStreetMap: user-generated street maps. IEEE Pervasive Computing, 7 (4), 12–18. doi:https://doi.org/10.1109/MPRV.2008.80
  • He, K., et al., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 770–778.
  • Hoffmann, E.J., et al. 2019a. Model fusion for building type classification from aerial and street view images. Remote Sensing, 11 (11), 1259. doi:https://doi.org/10.3390/rs11111259
  • Hoffmann, E.J., Werner, M., and Zhu, X., 2019b. Mutual information analysis of social media images and building functions. In: IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan.
  • Hoffmann, E.J., Werner, M., and Zhu, X.X., 2019. Building instance classification using social media images. In: Joint urban remote sensing event. Vannes, France, 1–4.
  • Huang, Z., et al. 2020. An ensemble learning approach for urban land use mapping based on remote sensing imagery and social sensing data. Remote Sensing, 12 (19), 3254. doi:https://doi.org/10.3390/rs12193254
  • Iovan, C., et al., 2012. Classification of urban scenes from geo-referenced images in urban street-view context. In: 11th International Conference on Machine Learning and Applications, Vol. 2, Boca Raton, Florida, USA, 339–344.
  • Jiang, S., et al., 2015. Mining point-of-interest data from social networks for urban land use classification and disaggregation. Computers, Environment and Urban Systems, 53, 36–46. doi:https://doi.org/10.1016/j.compenvurbsys.2014.12.001
  • Kang, J., et al., 2018. Building instance classification using street view images. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 44–59. doi:https://doi.org/10.1016/j.isprsjprs.2018.02.006
  • Karasov, O., et al. 2019. Mapping the extent of land cover colour harmony based on satellite Earth observation data. GeoJournal, 84 (4), 1057–1072. doi:https://doi.org/10.1007/s10708-018-9908-x
  • Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. Lake Tahoe, USA, 1097–1105.
  • Lef’evre, S., et al. 2017. Toward seamless multiview scene analysis from satellite to street level. Proceedings of the IEEE, 105 (10), 1884–1899. doi:https://doi.org/10.1109/JPROC.2017.2684300
  • Leung, D. and Newsam, S., 2009. Proximate sensing using georeferenced community contributed photo collections. In: Proceedings of the 2009 International Workshop on Location Based Social Networks, Seattle, USA, 57–64.
  • Leung, D. and Newsam, S., 2012. Exploring geotagged images for land-use classification. In: Proceedings of the ACM Multimedia 2012 Workshop on Geotagging and Its Applications in Multimedia, Nara, Japan, 3–8.
  • Li, L.J., et al., 2010. Object bank: a high-level image representation for scene classification & semantic feature sparsification. In: Advances in Neural Information Processing Systems, Vancouver Canada, 1378–1386.
  • Li, X. and Zhang, C., 2016. Urban land use information retrieval based on scene classification of Google Street View images. In: SDW@ GIScience, Montreal, QC, Canada, 41–46.
  • Lin, T.Y., et al., 2014. Microsoft COCO: common objects in context. In: European Conference on Computer Vision. Zurich, Switzerland, 740–755.
  • Liu, D., et al., 2020. Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: a case study in the middle Yangtze River basin. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 337–351. doi:https://doi.org/10.1016/j.isprsjprs.2019.11.021
  • Liu, W., et al., 2016. SSD: single shot multibox detector. In: European Conference on Computer Vision. Amsterdam, The Netherlands, 21–37.
  • Machado, G., et al., 2020. AiRound and CV-BrCT: novel multi-view datasets for scene classification. arXiv preprint arXiv:2008.01133.
  • Mahabir, R., et al. 2020a. Crowdsourcing street view imagery: a comparison of mapillary and OpenStreetCam. ISPRS International Journal of Geo-Information, 9 (6), 341. doi:https://doi.org/10.3390/ijgi9060341
  • Mahabir, R., et al. 2020b. Crowdsourcing street view imagery: a comparison of mapillary and OpenStreetCam. ISPRS International Journal of Geo-Information, 9 (6), 6.
  • Ministry of Infrastructure and the Environment, 2020. Addresses and buildings key register [online]. Available from: https://business.gov.nl/regulation/addresses-and-buildings-key-geo-register [Accessed Nov 2020].
  • Movshovitz-Attias, Y., et al., 2015. Ontological supervision for fine grained classification of street view storefronts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, Massachusetts, 1693–1702.
  • Munoz, J.E.V., et al., 2020. OpenStreetMap: challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Remote Sensing Magazine.
  • Neuhold, G., et al., 2017. The mapillary vistas dataset for semantic understanding of street scenes. In: Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 4990–4999.
  • Penatti, O.A., Nogueira, K., and Dos Santos, J.A., 2015. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, Massachusetts, 44–51.
  • Qiao, Z., et al., 2021. Attention pyramid module for scene recognition. In: International Conference on Pattern Recognition 2020, 10–15 January, Milan, Italy.
  • Qiao, Z., Yuan, X., and Elhoseny, M., 2020. Urban scene recognition via deep network integration. In: International Conference on Urban Intelligence and Applications. Taiyuan, China, 135–149.
  • Redmon, J. and Farhadi, A., 2018. Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767.
  • Ren, S., et al., 2015. Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Pprocessing Systems. Montréal CANADA, 91–99.
  • Rupali, A.S. and Patil, D., 2016. A mechanism for learning and recognition of on-premise signs from street view images. In: Symposium on Colossal Data Analysis and Networking. Indore, MP, India, 1–4.
  • Säynäjoki, E.-S., Heinonen, J., and Junnila, S., 2014. The power of urban planning on environmental sustainability: a focus group study in Finland. Sustainability, 6 (10), 6622–6643. doi:https://doi.org/10.3390/su6106622
  • Sharifi Noorian, S., et al., 2020. Detecting, classifying, and mapping retail storefronts using street-level imagery. In: Proceedings of the 2020 International Conference on Multimedia Retrieval. Dublin, Ireland, 495–501.
  • Shrivastava, A., Gupta, A., and Girshick, R., 2016. Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, 761–769.
  • Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Srivastava, S., et al., 2018a. Land-use characterisation using Google Street View pictures and OpenStreetMap. In: Proceedings of the association of Geographic Information Laboratories in Europe Conference. Lund, Sweden.
  • Srivastava, S., et al., 2018b. Multilabel building functions classification from ground pictures using convolutional neural networks. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. Seattle, WA, 43–46.
  • Srivastava, S., John E., Vargas M., Sylvain L., and Devis T. 2020. Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data. International Journal of Geographical Information Science, 34(6), 1117–1136.
  • Srivastava, S., Vargas-Muñoz, J.E., and Tuia, D., 2019. Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution. Remote Sensing of Environment, 228, 129–143. doi:https://doi.org/10.1016/j.rse.2019.04.014
  • Terroso‐Saenz, F., Muñoz, A., and Arcas, F., 2021. Land-use dynamic discovery based on heterogeneous mobility sources. International Journal of Intelligent Systems, 36 (1), 478–525. doi:https://doi.org/10.1002/int.22307
  • Tian, Y., Chen, C., and Shah, M., 2017. Cross-view image matching for geo-localization in urban environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Venice, Italy, 3608–3616.
  • Tracewski, L., Bastin, L., and Fonte, C.C., 2017. Repurposing a deep learning network to filter and classify volunteered photographs for land cover and land use characterization. Geo-spatial Information Science, 20 (3), 252–268. doi:https://doi.org/10.1080/10095020.2017.1373955
  • Tsai, T.-H., et al. 2014. Learning and recognition of on-premise signs from weakly labeled street view images. IEEE Transactions on Image Processing, 23 (3), 1047–1059. doi:https://doi.org/10.1109/TIP.2014.2298982
  • Van De Sande, K., Gevers, T., and Snoek, C., 2009. Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (9), 1582–1596. doi:https://doi.org/10.1109/TPAMI.2009.154
  • Vargas Muñoz, J.E., et al., 2020. OpenStreetMap: challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Remote Sensing Magazine.
  • Vargas-Muñoz, J.E., et al., 2019. Correcting rural building annotations in OpenStreetMap using convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 283–293. doi:https://doi.org/10.1016/j.isprsjprs.2018.11.010
  • Wang, Q., Zhou, C., and Xu, N., 2017. Street view image classification based on convolutional neural network. In: IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference. Chongqing, China, 1439–1443.
  • Wang, X. and Hofe, R., 2008. Research methods in urban and regional planning. Springer Science & Business Media. https://doi.org/10.1007/978-3-540-49658-8.
  • Wikipedia, 2020. Coverage of Google Street View [online]. Available from: https://en.wikipedia.org/w/index.php?&oldid=909938748 [Accessed Nov 2020].
  • Workman, S., Zhai, M., Crandall, D. J., and Jacobs, N. 2017. A Unified Model for Near and Remote Sensing. In: Proceedings of the IEEE International Conference on Computer Vision(ICCV). 2707–2716.
  • Xiao, J., et al., 2010. Sun database: large-scale scene recognition from abbey to zoo. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 3485–3492.
  • Ye, Y., An, Y., Chen, B., et al., 2019. Land use classification from social media data and satellite imagery. The Journal of Supercomputing, 76,  777–792.
  • You, Q., et al., 2015. Robust image sentiment analysis using progressively trained and domain transferred deep networks. In: Twenty-ninth AAAI Conference on Artificial Intelligence. Austin Texas, USA.
  • Yuan, X., et al., 2002. Mining negative association rules. In: Proceedings ISCC 2002 Seventh International Symposium on Computers and Communications. Jul. Taormina-Giardini Naxos, Italy, 623–628.
  • Yuan, X. and Sarma, V., 2011. Automatic urban water-body detection and segmentation from sparse ALSM data via spatially constrained model-driven clustering. IEEE Geoscience and Remote Sensing Letters, 8 (1), 73–77. doi:https://doi.org/10.1109/LGRS.2010.2051533
  • Yuan, X., Shi, J., and Gu, L., 2021. A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Systems with Applications, 169, 114417. doi:https://doi.org/10.1016/j.eswa.2020.114417
  • Zamir, A.R., Darino, A., and Shah, M., 2011. Street view challenge: identification of commercial entities in street view imagery. In: 10th International Conference on Machine Learning and Applications and Workshops, Vol. 2, 380–383.
  • Zhang, W., et al., 2017a. Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View. Computers, Environment and Urban Systems, 64, 215–228. doi:https://doi.org/10.1016/j.compenvurbsys.2017.03.001
  • Zhang, W., et al., 2017b. Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York City for urban land use classification in Brooklyn, New York City. Data in Brief, 12, 175–179. doi:https://doi.org/10.1016/j.dib.2017.04.002
  • Zhang, Y., Jin, R., and Zhou, Z.H., 2010. Understanding bag-of-words model: a statistical framework. International Journal of Machine Learning and Cybernetics, 1 (1–4), 43–52.
  • Zhao, K., et al., 2020. Bounding boxes are all we need: street view image classification via context encoding of detected buildings. arXiv preprint arXiv:2010.01305
  • Zhou, B., et al., 2014. Learning deep features for scene recognition using places database. In: Advances in Neural Information Processing Systems. Montréal CANADA, 487–495.
  • Zhou, B., et al., 2016. Places: an image database for deep scene understanding. arXiv preprint arXiv:1610.02055.
  • Zhu, Y., Deng, X., and Newsam, S., 2019. “Fine-grained land use classification at the city scale using ground-level images.” In :IEEE Transactions on Multimedia, vol. 21, no. 7, 1825–1838.
  • Zhu, Y. and Newsam, S., 2015. Land use classification using convolutional neural networks applied to ground-level images. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, Washington, USA, 61.

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