Browse > Article
http://dx.doi.org/10.7848/ksgpc.2019.37.6.517

Spatiotemporal Resolution Enhancement of PM10 Concentration Data Using Satellite Image and Sensor Data in Deep Learning  

Baek, Chang-Sun (Dept. of Geoinformation Engineering, Sejong University)
Yom, Jae-Hong (Dept. of Environment, Energy & Geoinformatics, Sejong University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.6, 2019 , pp. 517-523 More about this Journal
Abstract
PM10 concentration is a spatiotemporal phenomenta and capturing data for such continuous phenomena is a difficult task. This study designed a model that enhances spatiotemporal resolution of PM10 concentration levels using satellite imagery, atmospheric and meteorological sensor data, and multiple deep learning models. The designed deep learning model was trained using input data whose factors may affect concentration of PM10 such as meteorological conditions and land-use. Using this model, PM10 images having 15 minute temporal resolution and 30m×30m spatial resolution were produced with only atmospheric and meteorological data.
Keywords
$PM_{10}$; Deep Learning; Satellite Image; Sensor Data; Spatiotemporal Resolution;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Abderrahim, H., Chellali, M.R., and Hamou, A. (2016), Forecasting PM 10 in Algiers: Efficacy of multilayer perceptron networks, Environmental Science and Pollution Research, Vol. 23, No. 2, pp. 1634-1641.   DOI
2 Hochreiter, S. and Jurgen S. (1997), Long short-term memory, Neural Computation, Vol. 9, No. 8, pp. 1735-1780.   DOI
3 Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., and Brasseur, O. (2005), A neural network forecast for daily average PM10 concentrations in Belgium, Atmospheric Environment, Vol. 39, No. 18, pp. 3279-3289.   DOI
4 Kingma, D.P. and Welling, M. (2013), Auto-encoding variational bayes, 2nd Proceedings of the International Conference on Learning Representations, ICLR, 2-4 May, Scottsdale, Arizona, USA
5 Kingma, D.P., Mohamed, S., Rezende, D.J., and Welling, M. (2014), Semi-supervised learning with deep generative models, In Advances in Neural Information Processing Systems, pp. 3581-3589.
6 Kloog, I., Koutrakis, P., Coull, B.A., Lee, H.J., and Schwartz, J. (2011), Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements, Atmospheric Environment, Vol. 45, No. 35, pp. 6267-6275.   DOI
7 Krawczyk, B. (2016), Learning from imbalanced data: open challenges and future directions, Progress in Artificial Intelligence, Vol. 5, No. 4, pp. 221-232.   DOI
8 Le, V.D. (2019), Spatiotemporal Deep Learning Model for Citywide Air Pollution Interpolation and Prediction, Master's thesis, Seoul National University, Seoul, Korea, 52p.
9 Saleh, S.A.H. and Hasan, G. (2014), Estimation of PM10 concentration using ground measurements and Landsat 8 OLI satellite image, Journal of Geophysics and Remote Sensing, Vol. 3 No. 2, pp. 2169-0049.
10 Saraswat, I., Mishra, R.K., and Kumar, A. (2017), Estimation of PM10 concentration from Landsat 8 OLI satellite imagery over Delhi, India, Remote Sensing Applications: Society and Environment, Vol. 8, pp. 251-257.   DOI
11 Shahraiyni, H.T. and Sodoudi, S. (2016), Statistical modeling approaches for PM10 prediction in urban areas: A review of 21st-century studies, Atmosphere, Vol. 7, No. 2, 15p.   DOI
12 Wen, C., Liu, S., Yao, X., Peng, L., Li, X., Hu, Y., and Chi, T. (2019), A novel spatiotemporal convolutional long short-term neural network for air pollution prediction, Science of The Total Environment, Vol. 654, pp. 1091-1099.   DOI
13 Yao, L., Lu, N., and Jiang, S. (2012), Artificial neural network (ANN) for multi-source PM2.5 estimation using surface, MODIS, and Fig.rological data, International Conference on Biomedical Engineering and Biotechnology, IEEE, 28-30 May, Macau, China, pp. 1228-1231.
14 Zhou, Y., Chang, F.J., Chang, L.C., Kao, I.F., and Wang, Y.S. (2019), Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts, Journal of Cleaner Production, Vol. 209, pp. 134-145.   DOI