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Geospatial constrained optimization to simulate and predict spatiotemporal trends of air pollutants 期刊论文
SPATIAL STATISTICS, 2021, 卷号: 45, 页码: 28
Authors:  Li, Lianfa
Favorite  |  View/Download:9/0  |  Submit date:2021/11/05
Constrained optimization  Domain knowledge  Spatiotemporal patterns  Air pollutant  Prediction  Uncertainty  
Encoder-Decoder Full Residual Deep Networks for Robust Regression and Spatiotemporal Estimation 期刊论文
Authors:  Li, Lianfa;  Fang, Ying;  Wu, Jun;  Wang, Jinfeng;  Ge, Yong
Favorite  |  View/Download:11/0  |  Submit date:2021/11/05
Bias  deep learning  encoder-decoder  full residual deep network  non-linear regression  prediction of satellite aerosol optical depth (AOD) and PM2.5  spatiotemporal modeling  
Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks 期刊论文
REMOTE SENSING OF ENVIRONMENT, 2021, 卷号: 254, 页码: 22
Authors:  Li, Lianfa;  Wu, Jiajie
Favorite  |  View/Download:16/0  |  Submit date:2021/03/15
OMI-NO2 columns  Imputation of missing values  Full residual deep network  Bagging  Ground-level NO2 estimation  Traffic and land-use variables  Uncertainty  
Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke 期刊论文
ENVIRONMENT INTERNATIONAL, 2020, 卷号: 145, 页码: 16
Authors:  Li, Lianfa;  Girguis, Mariam;  Lurmann, Frederick;  Pavlovic, Nathan;  McClure, Crystal;  Franklin, Meredith;  Wu, Jun;  Oman, Luke D.;  Breton, Carrie;  Gilliland, Frank;  Habre, Rima
Favorite  |  View/Download:69/0  |  Submit date:2021/03/16
PM2.5  Machine learning  Air pollution exposure  Wildfires  Remote sensing  California  High spatiotemporal resolution  
Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 18, 页码: 21
Authors:  Wang, Chengyi;  Li, Lianfa
Favorite  |  View/Download:11/0  |  Submit date:2021/03/16
multiple scales  residual deep ensemble learning  regularizer  shape representation  semantic segmentation of buildings  
Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling 期刊论文
REMOTE SENSING OF ENVIRONMENT, 2020, 卷号: 237, 页码: 17
Authors:  Li, Lianfa;  Franklin, Meredith;  Girguis, Mariam;  Lurmann, Frederick;  Wu, Jun;  Pavlovic, Nathan;  Breton, Carrie;  Gilliland, Frank;  Habre, Rima
Favorite  |  View/Download:17/0  |  Submit date:2020/05/19
Aerosol Optical Depth  MAIAC  MERRA-2 GMI Replay Simulation  Deep learning  Downscaling  Missingness imputation  Air quality  
Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 3, 页码: 20
Authors:  Li, Lianfa
Favorite  |  View/Download:15/0  |  Submit date:2020/05/19
parameter inversion  aerosol optical depth  PBLH  ground-based AOD  PM2  5  automatic differentiation  
A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 2, 页码: 27
Authors:  Li, Lianfa
Favorite  |  View/Download:14/0  |  Submit date:2020/05/19
PM2.5  satellite AOD  deep learning  autoencoder  residual network  exposure estimation  high spatiotemporal resolution  
Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images 期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 18, 页码: 24
Authors:  Li, Lianfa
Favorite  |  View/Download:18/0  |  Submit date:2020/05/19
residual learning  autoencoder  multiscale  atrous spatial pyramid pooling  semantic segmentation  remotely sensed land-use images  
Cluster-based bagging of constrained mixed-effects models for high spatiotemporal resolution nitrogen oxides prediction over large regions 期刊论文
ENVIRONMENT INTERNATIONAL, 2019, 卷号: 128, 页码: 310-323
Authors:  Li, Lianfa;  Girguis, Mariam;  Lurmann, Frederick;  Wu, Jun;  Urman, Robert;  Rappaport, Edward;  Ritz, Beate;  Franklin, Meredith;  Breton, Carrie;  Gilliland, Frank;  Habre, Rima
Favorite  |  View/Download:38/0  |  Submit date:2019/09/24
Air pollution  Nitrogen oxides  Spatiotemporal variability  Generalization  Machine learning  Cluster methods