Cargando…

Exploring Spatial Influence of Remotely Sensed PM(2.5) Concentration Using a Developed Deep Convolutional Neural Network Model

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotel...

Descripción completa

Detalles Bibliográficos
Autores principales:	Li, Junming, Jin, Meijun, Li, Honglin
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2019
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6388139/ https://www.ncbi.nlm.nih.gov/pubmed/30720752 http://dx.doi.org/10.3390/ijerph16030454

Ejemplares similares

Spatiotemporal Variability of Remotely Sensed PM(2.5) Concentrations in China from 1998 to 2014 Based on a Bayesian Hierarchy Model
por: Li, Junming, et al.
Publicado: (2016)

Eigenvector Spatial Filtering Regression Modeling of Ground PM(2.5) Concentrations Using Remotely Sensed Data
por: Zhang, Jingyi, et al.
Publicado: (2018)

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM(2.5) Mapping
por: Shen, Huanfeng, et al.
Publicado: (2019)

Fusion of Environmental Sensing on PM(2.5) and Deep Learning on Vehicle Detecting for Acquiring Roadside PM(2.5) Concentration Increments
por: Wang, Wen-Cheng Vincent, et al.
Publicado: (2020)

A Spatial–Temporal Causal Convolution Network Framework for Accurate and Fine-Grained PM(2.5) Concentration Prediction
por: Lin, Shaofu, et al.
Publicado: (2022)

Incorporation of Remote PM(2.5) Concentrations into the Downscaler Model for Spatially Fused Air Quality Surfaces
por: Gantt, Brett, et al.
Publicado: (2020)

Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks
por: Tian, Tian, et al.
Publicado: (2018)

Spatiotemporal Patterns of Ground Monitored PM(2.5) Concentrations in China in Recent Years
por: Li, Junming, et al.
Publicado: (2018)

PM10 and PM2.5 real-time prediction models using an interpolated convolutional neural network
por: Chae, Sangwon, et al.
Publicado: (2021)

Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks
por: Yao, Heming, et al.
Publicado: (2022)

Estimation of PM2.5 Concentrations in China Using a Spatial Back Propagation Neural Network
por: Wang, Weilin, et al.
Publicado: (2019)

PM(2.5) Exposure and Health Risk Assessment Using Remote Sensing Data and GIS
por: Xu, Dan, et al.
Publicado: (2022)

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis
por: Kosarevych, Rostyslav, et al.
Publicado: (2022)

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification
por: Yu, Donghang, et al.
Publicado: (2020)

Spatial Pattern and Spillover of Abatement Effect of Chinese Environmental Protection Tax Law on PM(2.5) Pollution
por: Han, Fei, et al.
Publicado: (2022)

Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks
por: Giannopoulos, Michalis, et al.
Publicado: (2020)

Spatial–Temporal Variations in NO(2) and PM(2.5) over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing
por: Cai, Kun, et al.
Publicado: (2018)

Spatial Autocorrelation and Temporal Convergence of PM(2.5) Concentrations in Chinese Cities
por: Wang, Huan, et al.
Publicado: (2022)

PM(2.5) Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model
por: Zhang, Hang, et al.
Publicado: (2022)

The Driving Influence of Multi-Dimensional Urbanization on PM(2.5) Concentrations in Africa: New Evidence from Multi-Source Remote Sensing Data, 2000–2018
por: Wei, Guoen, et al.
Publicado: (2021)

Temporal and Spatial Features of the Correlation between PM(2.5) and O(3) Concentrations in China
por: Chen, Jiajia, et al.
Publicado: (2019)

Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network
por: Li, Chenming, et al.
Publicado: (2018)

An improved deep learning model for predicting daily PM2.5 concentration
por: Xiao, Fei, et al.
Publicado: (2020)

Application of convolutional neural network in fusion and classification of multi-source remote sensing data
por: Ye, Fanghong, et al.
Publicado: (2022)

Estimation of ambient PM(2.5) in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing
por: Li, Jing, et al.
Publicado: (2021)

Spatial and seasonal variability of the mass concentration and chemical composition of PM(2.5) in Poland
por: Rogula-Kozłowska, Wioletta, et al.
Publicado: (2013)

Spatial Variation of the Relationship between PM(2.5) Concentrations and Meteorological Parameters in China
por: Lin, Gang, et al.
Publicado: (2015)

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks
por: Otebolaku, Abayomi, et al.
Publicado: (2020)

Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM(2.5) Concentrations across the Continental United States
por: Lee, Seung-Jae, et al.
Publicado: (2012)

Deep neural network for remote-sensing image interpretation: status and perspectives
por: Li, Jiayi, et al.
Publicado: (2019)

PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network
por: Zhang, Li, et al.
Publicado: (2023)

Developing PM(2.5) and PM(10) prediction models on a national and regional scale using open-source remote sensing data
por: Mamić, Luka, et al.
Publicado: (2023)

Analysis of Airborne Particulate Matter (PM(2.5)) over Hong Kong Using Remote Sensing and GIS
por: Shi, Wenzhong, et al.
Publicado: (2012)

Spatial Equity of PM(2.5) Pollution Exposures in High-Density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China
por: Shan, Zhuoran, et al.
Publicado: (2022)

Research on Airplane and Ship Detection of Aerial Remote Sensing Images Based on Convolutional Neural Network
por: Cao, Changqing, et al.
Publicado: (2020)

Exhaustive Search of Correspondences between Multimodal Remote Sensing Images Using Convolutional Neural Network
por: Uss, Mykhail, et al.
Publicado: (2022)

Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network
por: Liu, Yun, et al.
Publicado: (2022)

W-Net: Convolutional neural network for segmenting remote sensing images by dual path semantics
por: Liu, Guangjie, et al.
Publicado: (2023)

Effects of transboundary PM(2.5) transported from China on the regional PM(2.5) concentrations in South Korea: A spatial panel-data analysis
por: Jun, Myung-Jin, et al.
Publicado: (2023)

Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images
por: Guo, Xianfei, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_n1im4lc59mt2l2ufipmobjt2ji