%0 Journal Article
%1 fan2017spatiotemporal
%A Fan, Junxiang
%A Li, Qi
%A Hou, Junxiong
%A Feng, Xiao
%A Karimian, Hamed
%A Lin, Shaofu
%D 2017
%I Copernicus GmbH
%J ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
%K air deep eva land lur network neural nn p2map pollution quality regression temporal use
%P 15
%T A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN
%V 4
@article{fan2017spatiotemporal,
added-at = {2018-02-21T11:51:34.000+0100},
author = {Fan, Junxiang and Li, Qi and Hou, Junxiong and Feng, Xiao and Karimian, Hamed and Lin, Shaofu},
biburl = {https://www.bibsonomy.org/bibtex/299ff924b29280bf19049d973334cfcdf/becker},
interhash = {bef7cf320d3f08e3eb503907c48ff97c},
intrahash = {99ff924b29280bf19049d973334cfcdf},
journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
keywords = {air deep eva land lur network neural nn p2map pollution quality regression temporal use},
pages = 15,
publisher = {Copernicus GmbH},
timestamp = {2018-02-21T11:53:20.000+0100},
title = {A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN},
volume = 4,
year = 2017
}