Social Media: New Perspectives to Improve Remote Sensing for Emergency Response

被引:49
作者
Li, Jun [1 ]
He, Zhi [1 ]
Plaza, Javier [2 ]
Li, Shutao [3 ]
Chen, Jinfen [1 ]
Wu, Henglin [1 ]
Wang, Yandong [4 ]
Liu, Yu [5 ]
机构
[1] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China
[2] Univ Extremadura, Hyperspectral Comp Lab, Dept Technol Comp & Commun, Escuela Politecn, E-10003 Caceres, Spain
[3] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
[4] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Hubei, Peoples R China
[5] Peking Univ, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China
基金
中国国家自然科学基金;
关键词
Deep learning; emergency response; remote sensing; social media; GEOGRAPHIC INFORMATION-SYSTEMS; EVENT DETECTION; LANDSAT TM; BIG DATA; OBJECT DETECTION; NEURAL-NETWORKS; FLOOD; GIS; TWITTER; WATER;
D O I
10.1109/JPROC.2017.2684460
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Remote sensing is a powerful technology for Earth observation (EO), and it plays an essential role in many applications, including environmental monitoring, precision agriculture, resource managing, urban characterization, disaster and emergency response, etc. However, due to limitations in the spectral, spatial, and temporal resolution of EO sensors, there are many situations in which remote sensing data cannot be fully exploited, particularly in the context of emergency response (i.e., applications in which real/near-real-time response is needed). Recently, with the rapid development and availability of social media data, new opportunities have become available to complement and fill the gaps in remote sensing data for emergency response. In this paper, we provide an overview on the integration of social media and remote sensing in time-critical applications. First, we revisit the most recent advances in the integration of social media and remote sensing data. Then, we describe several practical case studies and examples addressing the use of social media data to improve remote sensing data and/or techniques for emergency response.
引用
收藏
页码:1900 / 1912
页数:13
相关论文
共 128 条
  • [61] Processing Social Media Messages in Mass Emergency: A Survey
    Imran, Muhammad
    Castillo, Carlos
    Diaz, Fernando
    Vieweg, Sarah
    [J]. ACM COMPUTING SURVEYS, 2015, 47 (04)
  • [62] P3 systems: Putting the place back into social networks
    Jones, Q
    Grandhi, SA
    [J]. IEEE INTERNET COMPUTING, 2005, 9 (05) : 38 - 46
  • [63] Jung C.-T., 2015, P INT C LOC BAS SOC, P12
  • [64] Weighted joint-based human behavior recognition algorithm using only depth information for low-cost intelligent video-surveillance system
    Kim, Hanguen
    Lee, Sangwon
    Kim, Youngjae
    Lee, Serin
    Lee, Dongsung
    Ju, Jinsun
    Myung, Hyun
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2016, 45 : 131 - 141
  • [65] Kim Y., 2014, P 2014 C EMP METH NA, P1746, DOI [DOI 10.3115/V1/D14-1181, 10.3115/v1/D14-1181]
  • [66] A Tutorial on Interactive Sensing in Social Networks
    Krishnamurthy, Vikram
    Poor, H. Vincent
    [J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2014, 1 (01) : 3 - 21
  • [67] Kristóf D, 2015, INT GEOSCI REMOTE SE, P838, DOI 10.1109/IGARSS.2015.7325895
  • [68] Using tweets to support disaster planning, warning and response
    Landwehr, Peter M.
    Wei, Wei
    Kowalchuck, Michael
    Carley, Kathleen M.
    [J]. SAFETY SCIENCE, 2016, 90 : 33 - 47
  • [69] Gradient-based learning applied to document recognition
    Lecun, Y
    Bottou, L
    Bengio, Y
    Haffner, P
    [J]. PROCEEDINGS OF THE IEEE, 1998, 86 (11) : 2278 - 2324
  • [70] Spatial interpolation methods applied in the environmental sciences: A review
    Li, Jin
    Heap, Andrew D.
    [J]. ENVIRONMENTAL MODELLING & SOFTWARE, 2014, 53 : 173 - 189