Current Volume 8
AI-Powered Computer Vision for Remote Sensing and Carbon Emission Detection in Industrial and Urban Environments
Abstract
The rapid industrialization and urbanization of modern societies have led to a significant increase in carbon emissions, contributing to climate change and environmental degradation. Traditional monitoring methods often face limitations in spatial coverage, accuracy, and real-time detection. AI-powered computer vision, combined with remote sensing technologies, offers a transformative approach to monitoring and analyzing carbon emissions from industrial and urban sources. By leveraging machine learning and deep learning techniques, computer vision enables automated detection, classification, and quantification of emissions from satellite imagery, UAV-based sensors, and ground-level monitoring systems. This explores the integration of AI-driven computer vision in remote sensing for carbon emission detection. It examines key techniques such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models, which enhance the analysis of multispectral and hyperspectral imaging data. The application of these models allows for precise identification of emission hotspots, trend forecasting, and environmental impact assessments. Furthermore, the fusion of AI with Internet of Things (IoT) sensors and edge computing provides a real-time, scalable solution for continuous emission monitoring. Despite its advantages, AI-powered carbon emission detection faces challenges, including data availability, model interpretability, and ethical concerns regarding surveillance and data privacy. Addressing these issues through improved data integration, regulatory frameworks, and transparency in AI models is crucial for effective implementation. As AI technology advances, the potential for real-time, high-resolution carbon monitoring will improve, facilitating better regulatory compliance and supporting global sustainability efforts. This highlights the growing role of AI-powered computer vision in environmental monitoring and emphasizes the need for further research and policy development to harness its full potential in combating climate change.
Keywords
AI-powered computer vision. Remote sensing, Carbon emission detection, Urban environments
Citations
IRE Journals:
Jessica Obianuju Ojadi , Chinekwu Somtochukwu Odionu , Ekene Cynthia Onukwulu , Olumide Akindele Owulade
"AI-Powered Computer Vision for Remote Sensing and Carbon Emission Detection in Industrial and Urban Environments" Iconic Research And Engineering Journals Volume 7 Issue 10 2024 Page 490-509
IEEE:
Jessica Obianuju Ojadi , Chinekwu Somtochukwu Odionu , Ekene Cynthia Onukwulu , Olumide Akindele Owulade
"AI-Powered Computer Vision for Remote Sensing and Carbon Emission Detection in Industrial and Urban Environments" Iconic Research And Engineering Journals, 7(10)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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