Lichen Element Based Autonomous Air Pollution Monitoring Around Smart Cities – A Deep Learning Approach

Abstract

Increasing population across the globe affects the environment through the excessive usage of available natural resources and affects the quality of air to a greater extent. Air pollution has become most vulnerable for the existence of the living organisms. Smart cities projects concentrate on implementing the core technological infrastructure there by ensuring the quality of life to their citizens. Reducing the air pollution remains a main objective while implementing the smart city projects. Lichen elements are the natural bio indicators that are can be used as a natural indicator of climatic change and air pollution effects. In this proposed work lichen elements are located on the surface of the buildings across the smart cities. Advancements in hardware and computational software widens the usage of deep learning approaches for image classification. The proposed RESNET model is capable of receiving the input and extracting the color features out of the lichen images. The Unmanned Ariel Vehicle is an autonomous source for collecting the lichen images located on the surface of the buildings. The extracted features pass through the stack of convolution and max pool layers in RESNET architecture. Finally, the input image is flattened and classified based on the pollution level. The accuracy of the proposed architecture is validated with the traditional methods support vector machine and VGG-16 deep learning approach. The advanced computational techniques prove that the lichen images can be effectively used for air pollution monitoring around the smart cities.