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

被引:17
作者
Qiao, Zhinan [1 ]
Yuan, Xiaohui [1 ]
机构
[1] Univ North Texas, Dept Comp Sci & Engn, Denton, TX 76203 USA
关键词
Proximate sensing; urban land-use; volunteer geographic information; street view; STREET VIEW IMAGES; USE CLASSIFICATION; OPENSTREETMAP DATA; COVER; PHOTOGRAPHS; AERIAL;
D O I
10.1080/13658816.2021.1919682
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with the Global Positioning System (GPS) greatly proliferates proximate sensing images taken near or on the ground at a close distance to urban targets. Studies leveraging proximate sensing images have demonstrated great potential to address the need for local data in the urban land-use analysis. This paper reviews and summarizes the state-of-the-art methods and publicly available data sets from proximate sensing to support land-use analysis. We identify several research problems in the perspective of examples to support the training of models and means of integrating diverse data sets. Our discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing images in urban land-use studies.
引用
收藏
页码:2129 / 2148
页数:20
相关论文
共 89 条
  • [1] [Anonymous], 2012, Proceedings of the ACM Multimedia 2012 Workshop on Geotagging and its Applications in Multimedia, DOI DOI 10.1145/2390790.2390794
  • [2] Investigating the Feasibility of Geo-Tagged Photographs as Sources of Land Cover Input Data
    Antoniou, Vyron
    Fonte, Cidalia Costa
    See, Linda
    Estima, Jacinto
    Arsanjani, Jamal Jokar
    Lupia, Flavio
    Minghini, Marco
    Foody, Giles
    Fritz, Steffen
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2016, 5 (05):
  • [3] Arsanjani JJ, 2015, LECT NOTES GEOINF CA, P37, DOI 10.1007/978-3-319-14280-7_3
  • [4] Toward mapping land-use patterns from volunteered geographic information
    Arsanjani, Jamal Jokar
    Helbich, Marco
    Bakillah, Mohamed
    Hagenauer, Julian
    Zipf, Alexander
    [J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2013, 27 (12) : 2264 - 2278
  • [5] ATTOM Data Solutions, 2020, POINTS INT DAT
  • [6] Avila S., 2011, 2011 18th IEEE International Conference on Image Processing (ICIP 2011), P2909, DOI 10.1109/ICIP.2011.6116268
  • [7] SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
    Badrinarayanan, Vijay
    Kendall, Alex
    Cipolla, Roberto
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) : 2481 - 2495
  • [8] Bansal Aayush, 2017, ARXIV PREPRINT ARXIV
  • [9] Bromley J., 1993, International Journal of Pattern Recognition and Artificial Intelligence, V7, P669, DOI 10.1142/S0218001493000339
  • [10] Cao R, 2018, INT WORK CONTENT MUL