Outdoor Air Quality Inference from Single Image

Along with rapid urbanization and industrialization processes, many developing countries are suffering from air pollution. Air quality varies non-linearly, the effective range of an air quality monitoring station is limited. While there are seldom air quality monitoring stations in cities, it is difficult to know the exact air quality of everywhere. How to obtain the air quality fast and conveniently will attract much attention. In this paper, we present an air quality inference approach based on air quality index(AQI) decision tree from a single image. We first extract several corresponding features such as medium transmission, power spectrum slope, contrast, and saturation from the single image. Then we construct a decision tree of AQI values, in accordance with the distance between the features we extract previously. For each none-leaf node of the decision tree, we use five classifiers to choose the next node respectively. We collect a dataset of high quality registered and calibrated images named Outdoor Air Quality Image Set(OAQIS). The dataset covers a wide range of daylight illumination and air pollution conditions. We evaluate our approach on the dataset, the results show the effective of our method.