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
  • [101] Harvesting ambient geospatial information from social media feeds
    Stefanidis, Anthony
    Crooks, Andrew
    Radzikowski, Jacek
    [J]. GEOJOURNAL, 2013, 78 (02) : 319 - 338
  • [102] The convergence of GIS and social media: challenges for GIScience
    Sui, Daniel
    Goodchild, Michael
    [J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2011, 25 (11) : 1737 - 1748
  • [103] Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs
    Tajbakhsh, Nima
    Suzuki, Kenji
    [J]. PATTERN RECOGNITION, 2017, 63 : 476 - 486
  • [104] Social-Network-Sourced Big Data Analytics
    Tan, Wei
    Blake, M. Brian
    Saleh, Iman
    Dustdar, Schahram
    [J]. IEEE INTERNET COMPUTING, 2013, 17 (05) : 62 - 69
  • [105] Tyshchuk Y., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), P818, DOI 10.1109/HICSS.2012.536
  • [106] Learning from big data with uncertainty - editorial
    Wang, Xizhao
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2015, 28 (05) : 2329 - 2330
  • [107] An efficient method for mapping flood extent in a coastal floodplain using Landsat TM and DEM data
    Wang, Y
    Colby, JD
    Mulcahy, KA
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2002, 23 (18) : 3681 - 3696
  • [108] Wang Y., 2013, J LUOYANG I SCI TECH, V23, P64
  • [109] Advances in Remote Sensing of Flooding
    Wang, Yong
    [J]. WATER, 2015, 7 (11) : 6404 - 6410
  • [110] An Integrated WebGIS Framework for Volunteered Geographic Information and Social Media in Soil and Water Conservation
    Werts, Joshua D.
    Mikhailova, Elena A.
    Post, Christopher J.
    Sharp, Julia L.
    [J]. ENVIRONMENTAL MANAGEMENT, 2012, 49 (04) : 816 - 832