AirMapNet: Smart Air Pollution Mapping Using Deep Learning

Pollution detection is a global concern since pollutants are always harmful to human and environmental health. Traditional monitoring has a great dependency on costly sensor networks with limited geographic coverage. Lacking an efficient and scalable method, the research proposes employing an AIenhanced image processing framework to sense pollution from images. The research is filling the gap of the dominant approaches that have an enormous dependency on physical sensors. It seeks to leverage the latest strengths of image datasets in directly detecting pollution indicators. AirMapNet is a scalable and lightweight deep learning-based method for image-based detection of air pollution, circumventing the unavoidable drawbacks of sensor-dependent traditional methods. With its ultralightweight design with just 1.56 million parameters, overall classification accuracy is as high as 93.59% and F1-score up to 0.821 in terms of AQI prediction. Its performance enables it to be ready for real-time deployment on edge devices for innovative air quality monitoring applications. This yields high classification accuracy, high precision, and low-cost pollution monitoring for influencing urban planning and industrial policymaking. AirMapNet can precisely detect pollution indicators in image data for further environmental monitoring enhancement and extension of traditional sensor networks.