Abstract
PM2.5, a dangerous air pollutant, mapping on a city level scale, plays a crucial role in the development of sustainable policies toward balanced ecology and a pollution-free society. Recently, multispectral and hyperspectral satellite imagery promise a high capability toward detecting the places with soaring atmospheric pollution and aerosol information. The multispectral imagery uses the ambient surface reflectance from the surface of the earth in the visible spectrum bands. LANDSAT-8 satellite provides multispectral observations over the surface of the earth with 30m resolution. We develop various machine learning models for PM2.5 estimation for one of the most highly polluted cities in the world, Delhi, the captial city of India using LANDSAT-8 observations and ground-level PM2.5 data. A feasible multispectral-based PM2.5 estimation model is established in this study, which promises high-resolution PM2.5 mapping from LANDSAT-8 imagery with an acceptable level of accuracy.
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Ejurothu, P.S.S., Mandal, S., Thakur, M. (2023). A Machine Learning Approach for PM2.5 Estimation for the Capital City of New Delhi Using Multispectral LANDSAT-8 Satellite Observations. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_31
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DOI: https://doi.org/10.1007/978-981-19-7867-8_31
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