Waste and Natural Disaster Image Classification using CNN

Artificial Intelligence

Natural calamities can exert a deep and extensive impact on human beings, influencing people on personal, communal, and societal levels across various dimensions such as physical, mental, economic, and social aspects. The utilization of machine learning algorithms to categorize images depicting natural disasters can prove highly beneficial to humans, offering valuable insights and aid in managing and responding to disasters effectively. Each year, the Earth generates 2.01 billion metric tons of municipal solid waste, and a conservative estimate suggests that at least 33 percent of this waste is not managed in an environmentally responsible manner. Waste management is a worldwide problem that has an impact on all nations, with each day producing 4.4 pounds of waste per person [1]. The categorization of waste images holds promise for improving waste management procedures, elevating recycling percentages, minimizing environmental pollution, and promoting sustainable development. A Convolutional Neural Network (CNN), a subset of machine learning algorithms and artificial neural networks tailored for image-related tasks, has played a transformative role in computer vision and image processing applications. This is attributed to its capacity to autonomously acquire and extract meaningful features from images. In this article, we are proposing for a CNN system for classifying Waste and Natural Disaster images.

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