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On Estimating Air Pollution from Photos Using Convolutional Neural Network

Chao Zhang, Junchi Yan, Changsheng Li, Xiaoguang Rui, Liang Liu, Rongfang Bie
2016 Proceedings of the 2016 ACM on Multimedia Conference - MM '16  
Focusing on this immediate problem, this paper devises an effective convolutional neural network to estimate air's quality based on photos.  ...  Different from using expensive or unreliable methods like sensor-based or social network based one, photo based air pollution estimation is a promising direction, while little work has been done up to  ...  Empirical results show the feasibility of image based pollution estimation. • A novel convolutional neural network is tailored to the air pollution estimation task with two ingredients.  ... 
doi:10.1145/2964284.2967230 dblp:conf/mm/ZhangYLRLB16 fatcat:lqwiz4t65babhi4cojww6th7oy

Using user generated online photos to estimate and monitor air pollution in major cities

Yuncheng Li, Jifei Huang, Jiebo Luo
2015 Proceedings of the 7th International Conference on Internet Multimedia Computing and Service - ICIMCS '15  
Our experiments based on both synthetic and real photos have shown the promise of this image-based approach to estimating and monitoring air pollution.  ...  Recently, a number of studies have dealt with air quality and air pollution.  ...  In parallel, we estimate the depth map based on the Deep Convolutional Neural Fields (DCNF) [7] .  ... 
doi:10.1145/2808492.2808564 dblp:conf/icimcs/LiHL15 fatcat:iqlkqi4bsraq5jkhztxvgamfky

Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast [article]

Qi Zhang, Jacqueline CK Lam, Victor OK Li, Yang Han
2020 arXiv   pre-print
In this paper, we proposea novel hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)together to forecast air quality at high-resolution.  ...  Our model can utilize the spatial correlation characteristic of our air pollutant datasetsto achieve higher forecasting accuracy than existing deep learning models of air pollution forecast.  ...  We would like to acknowledge Beijing Municipal Environment Monitoring Center and National Meteorological Information Center, China for publicizing air quality and meteorological data of Beijing.  ... 
arXiv:2001.11957v1 fatcat:2y7ew2ckovcozgyg3jm3yn46pq

Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting

Kıymet Kaya, Şule Gündüz Öğüdücü
2020 Scientific Reports  
DFS model is a hybrid & flexible deep model including Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN).  ...  Our study uses real world hourly data from Istanbul, Turkey between 2014 and 2018 to forecast the air pollution 4, 12, and 24 hours before.  ...  Acknowledgements We're thankful to the Turkish State Meteorological Service and Istanbul Metropolitan Municipality for providing the meteorological and traffic data used in this study.  ... 
doi:10.1038/s41598-020-60102-6 pmid:32098977 fatcat:tmvdfax4v5cvdlnanvojog3syy

Estimation of Ground PM2.5 Concentrations in Pakistan Using Convolutional Neural Network and Multi-Pollutant Satellite Images

Maqsood Ahmed, Zemin Xiao, Yonglin Shen
2022 Remote Sensing  
A Convolutional Neural Network (CNN) P-CNN model is presented in this paper, which uses seven different pollutant satellite images, such as Aerosol index (AER AI), Methane (CH4), Carbon monoxide (CO),  ...  This study presents robust model using satellite images, useful for estimating PM2.5 concentrations.  ...  This paper proposes a deep convolutional neural network model to estimate PM2.5 concentrations from seven given input images.  ... 
doi:10.3390/rs14071735 fatcat:kla7yu4l5feo7fqdq6wyfql4b4

MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images [article]

Michael Steininger, Konstantin Kobs, Albin Zehe, Florian Lautenschlager, Martin Becker, Andreas Hotho
2020 arXiv   pre-print
It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering.  ...  Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations.  ...  [36] , who proposed models to estimate air haze level using photos from, for example, mobile phones or webcams.  ... 
arXiv:2002.07493v1 fatcat:qi5opkmvanhudot6qnsptiygxy

Deep Learning from Spatio-temporal Data using Orthogonal Regularizaion Residual CNN for Air Prediction

Lei Zhang, Dong Li, Quansheng Guo
2020 IEEE Access  
Deep Convolutional Neural Network (CNN) is presented to capture the complex spatio-temporal relation of the dynamic biased meteorological data.  ...  Air pollution is harmful to human health and restricts economic development, so predicting when and where air pollution will occur is a challenging and important issue, especially in fields of urban planning  ...  [5] designed a neural network which has two branches for attention box prediction (ABP) and aesthetics assessment (AA) on photo cropping.  ... 
doi:10.1109/access.2020.2985657 fatcat:ufaoyp2yqncrnkdwsw2m4spthy

2020 Index IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 4

2020 IEEE Transactions on Emerging Topics in Computational Intelligence  
., +, TETCI Aug. 2020 555-570 Convolutional neural nets DFUNet: Go?. Liang, S.D., TETCI April 2020 171-179 Photobomb Defusal Expert: Automatically Remove Distracting People From Photos.  ...  ., +, TETCI Feb. 2020 51-60 Photobomb Defusal Expert: Automatically Remove Distracting People From Photos.  ... 
doi:10.1109/tetci.2020.3042423 fatcat:qj6bpqfey5gpjhqe7zvgg644l4

Application of Photo Texture Analysis and Weather Data in Assessment of Air Quality in Terms of Airborne PM10 and PM2.5 Particulate Matter

Monika Chuchro, Wojciech Sarlej, Marta Grzegorczyk, Karolina Nurzyńska
2021 Sensors  
The research aimed to create regression and classification models for PM10 and PM2.5 estimation based on sky photos and basic weather data.  ...  For this research, one short video with a resolution of 1920 × 1080 px was captured each day. From each film, only five frames were used, the information from which was averaged.  ...  In neural network-based methods, Vahdatpour et al. proposed a method to estimate pollution forecast with Convolutional Neural Network based on sky images and Gabor transform [13, 14] .  ... 
doi:10.3390/s21165483 pmid:34450925 pmcid:PMC8399617 fatcat:wobixflpure5dgtvcm4cqvsu7y

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Sami Kabir, Raihan Ul Islam, Mohammad Shahadat Hossain, Karl Andersson
2020 Sensors  
We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach.  ...  Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the  ...  Acknowledgments: This research is based on the Master's Thesis [60] of Sami Kabir, conducted at the Pervasive and Mobile Computing Laboratory, Luleå University of Technology, Skellefteå, Sweden.  ... 
doi:10.3390/s20071956 pmid:32244380 fatcat:efkzbpmr6rgapeqm62qz7otksy

Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [article]

Yi Liu, Jiangtian Nie, Xuandi Li, Syed Hassan Ahmed, Wei Yang Bryan Lim, Chunyan Miao
2020 arXiv   pre-print
For ground sensing systems, we propose a Graph Convolutional neural network-based Long Short-Term Memory (GC-LSTM) model to achieve accurate, real-time and future AQI inference.  ...  Specifically, in the air, this framework leverages a light-weight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by Unmanned Aerial Vehicles  ...  Inspired by the previous work, we leverage the FL technique to learning models that estimate air quality by using UAVtaking photos.  ... 
arXiv:2007.12004v1 fatcat:c2dgwdpncvfbxibjohrquhzhc4

Application of Convolution Neural Networks and Hydrological Images for the Estimation of Pollutant Loads in Ungauged Watersheds

Chul Min Song
2021 Water  
To this end, this study has used a convolution neural network (CNN), one of the deep learning algorithms, to reflect the topographical characteristic and had estimated the pollutant loads of ungauged watersheds  ...  To address this, previous studies have used conceptual and empirical models and have recently used artificial neural network models.  ...  In particular, this study aims to estimate the pollution load of ungauged watersheds by introducing the convolution neural network (CNN), a DNN algorithm, and use hydrological images as input data for  ... 
doi:10.3390/w13020239 fatcat:jnjhox7xu5d5dm2ayrhph2rho4

Air Quality Index Estimation Based on Image Analysis

Mohd Ashraf., Niket., Devender & Dr. Vinod Kumar
2021 International journal of modern trends in science and technology  
This will not only spread awareness about air pollution but also protect people from the harmful effects of air pollution. We have used Machine Learning to achieve this goal.  ...  Air pollution is an issue that is out of the control of an average citizen. Controlling air pollution requires preventive and control measures on a large scale implemented by the government.  ...  They extracted high-level features based on convolutional neural networks (CNN) and learned the mapping between the features and PM2.5 by support vector regression (SVR).  ... 
doi:10.46501/ijmtst0705029 fatcat:2ofzhzobgrcupp5obmdv2ccl5y

Advances in computational intelligence

Miguel Atencia, Gonzalo Joya, Francisco García-Lagos
2019 Neural computing & applications (Print)  
The 14th edition of the biennial International Work-Conference on Artificial Neural Networks (IWANN 2017) gathered together more than one hundred significant contributors in the fields of artificial neural  ...  methodology to efficiently set the parameters of a convolutional neural network and rank the importance of the involved variables, in the context of medical image interpretation.  ...  The 14th edition of the biennial International Work-Conference on Artificial Neural Networks (IWANN 2017) gathered together more than one hundred significant contributors in the fields of artificial neural  ... 
doi:10.1007/s00521-019-04324-4 fatcat:234n6cxenbfkzls3hp42mw7x3e

Implementation of Garbage Litter Detection using Image Processing with Novel Perspective of Software Development

Mohit Bohra
2021 International Journal for Research in Applied Science and Engineering Technology  
This review paper will throw light on our proposed system which we have designed and developed to monitor anyone who throws the trash outside the allotted area, using image processing, facial recognition  ...  The surveillance of communal dustbins by our system will increase sanity and prevent the trash from exposing in public areas.  ...  Image denoising has been taken care of using deep convolutional neural networks.  ... 
doi:10.22214/ijraset.2021.33635 fatcat:b6t43eqctfeanfc3yzwhn6y3b4
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