Solving visual pollution with deep learning: A new nexus in environmental management

Visual pollution is a relatively new concern amidst the existing plethora of mainstream environmental pollution, recommending the necessity for research to conceptualize, formalize, quantify and assess it from different dimensions. The purpose of this study is to create a new field of automated visual pollutant classification, harnessing the technological prowess of the 21st century for applications in environmental management. From the wide range of visual pollutants, four categories have been considered viz. (i) billboards and signage, (ii) telephone and communication wires, (iii) network and communication towers and (iv) street litter. The deep learning model used in this study simulates the human learning experience in the context of image recognition for visual pollutant classification by training and testing a convolutional neural…