Air Pollution Detection Using Image Processing

Air pollution has created significant implications for public health and well-being. The chapter looks into the major events of air pollution, its impact and various approaches to deal with the air pollution. Traditional methods of air pollution monitoring and intensity detection rely on stationary sensors and manual measurements, which are often limited in spatial coverage and real-time monitoring capabilities. This chapter discusses a methodology that analyzes images captured from various sources, such as satellite imagery or ground-based cameras, and accurately classify different types of air pollution, including smog, particulate matter, and industrial emissions. The goal is to provide an efficient and automated method for monitoring air quality, enabling early detection of pollution hotspots, and assisting in environmental management and public health efforts. The image and video processing model of air pollution detection has achieved the loss is 0.4434 and accuracy of 88.88%.