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In recent years, the frequent fouling accidents has posed a serious threat to people's life and property safety. Due to the wide distribution of pollution sources and variable meteorological factors, it is a very time-consuming and labour-intensive task to map the pollution distribution by traditional methods. In this work, a study on the mapping of pollution distribution based on satellite remote sensing is carried out in Yunnan Province, China, as an example. Several machine learning methods (e.g. KNN, SVM, etc.) are used to analyze the effects of conditions such as multiple air pollution data and meteorological data on pollution distribution map levels. The results indicate that the ensemble learning model has the highest accuracy of 71.2\% in this application. The new pollution distribution map using this classifier has 5,506 more pixels in the most severe pollution level than the traditional. Lastly, The remote sensing-based map and the manual measurement-based map were combin ....

South Korea , Gong Da , Mixture Density Networks , Anthropogenic Co , International Conference On Computer Distributed , Development Program Grant No , Yunnan Electric Power Company , Design Of Inversion Procedure For The Airborne , Company Power Grid , Yunnan Power Grid Company Ltd , Time Neural Networks , China Power , National Key Research , A Global Monthly Fossil Fuel Co , Observation Network , Temporally Weighted Neural Networks For Satellite , Yunnan Province , Drawing Method , Pollution Distribution , China High Air Pollutants , Multi Resolution Emission Inventory , Development Program , National Natural Science Foundation , Technology Project , Yunnan Power Grid Company , Google Earth Engine ,