LSTM models for spatiotemporal extrapolation of population data
Author:
Affiliation:
1. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR),Wessling-Oberpfaffenhofen,Germany,82234
2. University of Cambridge,Centre for Risk in the Built Environment,Cambridge,UK
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10144111/10144082/10144145.pdf?arnumber=10144145
Reference14 articles.
1. LandScan: A Global Population Database for Estimating Populations at Risk;dobson;Photogrammetric Engineering & Remote Sensing,2000
2. Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data
3. Convolutional lstm network: A machine learning approach for precipitation nowcasting;shi;Advances in neural information processing systems,2015
4. Long Short-Term Memory
5. The Prediction of Finely-Grained Spatiotemporal Relative Human Population Density Distributions in China
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