%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 }