Air pollution from stationary sources such as factories significantly impacts cities.Detecting and understanding these sources is challenging due to undocumented,emerging pollutants and a lack of consolidated data. This research aims to create acomprehensive stationary pollutant dataset with supplementary data from satellitedata. This dataset is specifically developed to use machine learning algorithms fortasks such as detecting and profiling source point sources in satellite imagery.We trained a U-Net model with an accuracy of 80% on the manually labelled dataset to automatically identify potential stationary sources of pollution. To facilitate integration with other datasets, a post-processing step converts the model's predictions into a geospatial format (GeoJSON) with location information. Combining this data on stationary sources with ground-based monitoring empowers stakeholders to gain a deeper understanding of a city's air pollution patterns.