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On Estimating Air Pollution from Photos Using Convolutional Neural Network
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
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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
2015
Proceedings of the 7th International Conference on Internet Multimedia Computing and Service - ICIMCS '15
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2015 pre-print arXiv:1508.05028v1 fatcat:zmimcryqfne7lpzevv6clf5cjm
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]
2020
arXiv
pre-print
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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
2020
Scientific Reports
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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
2022
Remote Sensing
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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]
2020
arXiv
pre-print
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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
2020
IEEE Access
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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
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., +, 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
2021
Sensors
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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
2020
Sensors
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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]
2020
arXiv
pre-print
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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
2021
Water
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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
2021
International journal of modern trends in science and technology
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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
2019
Neural computing & applications (Print)
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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
2021
International Journal for Research in Applied Science and Engineering Technology
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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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