Preface

The proposed book offers an in-depth examination of the most up-to-date advancements in remote sensing and intelligent systems. It includes practical case studies that demonstrate how these technologies are used in real-life situations. Additionally, the book engages in critical discussions regarding the ethical and technical obstacles faced in this field. The book's aspects render it an indispensable resource for comprehending how intelligent systems can contribute to sustainable development. The detailed overview of the chapters is as follows:

Chapter 1 explores the transformative role of artificial intelligence (AI) in environmental monitoring, particularly in industrial settings. This chapter delves into the integration of AI with remote sensing technologies, including satellite imagery, drones, and IoT sensors, to enhance data collection, analysis, and real-time monitoring. The chapter highlights how AI-driven approaches improve the accuracy and efficiency of detecting pollutants, deforestation, and biodiversity loss compared to traditional environmental surveillance methods. Real-world case studies illustrate the practical impact of AI in promoting environmental sustainability, while also addressing future challenges and directions in this rapidly evolving field.

Chapter 2 examines the various applications of AI in environmental monitoring, including air and water pollution detection, climate change prediction, biodiversity monitoring, and disaster management. It also examines the role of AI in optimizing natural resource management and supporting sustainable development. It emphasizes the importance of responsible AI development and deployment to avoid unintended consequences.

Chapter 3 explains the impact of Artificial Intelligence (AI) technologies on the enhancement of environmental monitoring activities. The focus will be on how AI technologies improve the management of natural resources and the control of environmental risks. This chapter examines examples of AI use in various environmental sectors, including but not limited to air pollution control, weather forecasting, and the prediction of extreme events such as climate change, where information is made available to users in real-time. Thanks to IoT and Big Data, it is therefore impossible to separate AI from the intake, processing, and output of data, especially when it comes to sustainable operations. The chapter further highlights the challenges that come with the use of AI, including data quality issues and ethical concerns, as it revisits how AI should be used in the next generation of environmental problem solutions.

Chapter 4 explores the transformative potential of artificial intelligence (AI) to support environmental sustainability through advanced data collection, analysis, and modeling. It enables state-of-the-art solutions by integrating IoT-driven data ingestion, advanced preprocessing, and machine learning algorithms to address major global challenges such as climate change and pollution. This research supports sustainable environmental management by suggesting actions and providing insights to policymakers and stakeholders. It is considered a step towards using artificial energy to achieve a strong and stable life.

Chapter 5 explores the powerful synergy between artificial intelligence and remote sensing technologies within the framework of Industry 6.0, with a focus on advancing environmental sustainability. This chapter examines how AI algorithms, coupled with high-resolution satellite imagery and sensor networks, are revolutionizing real-time environmental monitoring, resource optimization, and climate pattern prediction. Through case studies in areas such as deforestation tracking, air quality assessment, and precision agriculture, the chapter highlights the practical applications and potential of these technologies. Additionally, it discusses emerging trends, such as edge computing and blockchain integration for enhanced data security and processing efficiency. The chapter emphasizes both the transformative potential of AI-driven remote sensing and the ethical considerations necessary for its responsible application in fostering sustainable industries.

Chapter 6 explores the potential of geospatial technologies to transform the monitoring of one of our most destructive environmental problems—deforestation. The scope of this chapter is to determine the potential of intelligent systems, satellite imagery, and Geographic Information Systems (GIS) in forest loss detection, analysis, and management. Geospatial analysis: By combining environmental data with sophisticated monitoring tools, the analysis spurs policymakers, conservationists, and communities to act in ways that promote sustainable land use. This chapter focuses on how geospatial tools for foresight support effective mitigation of deforestation and environmental conservation towards a resilient future through technological innovation and cross-sector collaboration.

Chapter 7 explores the effects of climate change and the global increase in CO₂ concentrations by examining 10 years of data (2014–2024) that were sourced from Kaggle. The goal of the project is to estimate CO₂ trends and seasonal cycles for the upcoming year using five forecasting models: SARIMA, Prophet, LSTM, XGBoost, and ETS. R2, RMSE, and MAE were used to assess the model's performance. According to the results, LSTM is perfect for creating accurate CO₂ trend forecasts because it had the highest accuracy (MAE: 0.09, RMSE: 0.12, R2: 1.00). On the other hand, Prophet and XGBoost provided decent accuracy, while SARIMA fared poorly. The study emphasises the need for advanced prediction models to counteract the effects of climate change and the potential of LSTM to inform climate policies.

Chapter 8 delves into the transformative role of AI in environmental decision-making, highlighting its potential to address critical challenges such as pollution, biodiversity loss, and climate change. It explores AI-driven methodologies, including IoT-enabled real-time monitoring, multimodal data fusion, and explainable AI models, to provide actionable insights for sustainable practices. While emphasizing technological advancements, the chapter also addresses ethical considerations such as data privacy, environmental equity, and the ecological impact of AI infrastructure. Through comprehensive analysis and future directions, this chapter aims to bridge the gap between AI innovation and ecological sustainability.

Chapter 9 examines how Industry 6.0 technologies—such as IoT, AI, and cloud computing—are transforming higher education by enabling intelligent data visualization, automation, and personalized learning experiences. By integrating these advanced tools, universities can streamline administrative tasks, enhance educational quality, and better support students' academic journeys. The chapter provides a framework for implementing Industry 6.0 in academic settings, highlighting real-world applications, ethical considerations, and the need for secure data practices. This approach offers a pathway to more dynamic, data-driven university systems that foster innovation and adapt to the evolving needs of modern learners.

Chapter 10 explores the critical role of meteorological satellites in disaster management, environmental monitoring, and sustainable development. It delves into their history, technological components, and practical applications, emphasizing their significance in climate change detection and natural disaster prediction. Through case studies and detailed insights, the chapter highlights how satellite technology enhances our understanding of atmospheric phenomena and addresses global environmental challenges.

Chapter 11 delves into the transformative potential of Artificial Intelligence (AI) in enhancing weather prediction, with a focus on the application of Machine Learning (ML) and Deep Learning (DL) models. It presents a novel design based on the Bidirectional Long Short-term Memory (BiLSTM) deep learning framework for predicting average temperatures at rain gauge stations in the lower Mahanadi River basin. By integrating advanced clustering techniques with principal component analysis, we identify the most effective rain gauge stations: Kantamal, Kesinga, Salebhata, and Sundergarh.

Chapter 12 delves into the transformative potential of Generative Adversarial Networks (GANs) in enhancing low-light image quality and reconstructing missing data in environmental datasets. By training GANs to optimize brightness, contrast, and overall image clarity under challenging low-light conditions, this approach represents a significant leap forward in image enhancement technology. Moreover, it paves the way for groundbreaking applications of deep learning in key areas such as photography, security, and environmental science.