AI-powered urban infrastructure monitoring using RADAR-based remote sensing
-
Inderjeet Singh
,
Eakansh
Abstract
This chapter explores how artificial intelligence (AI) drives a paradigm shift in RADAR-based remote sensing for urban infrastructureurban infrastructure monitoring. Urban monitoring has shifted toward a predictive and proactive approach by harnessing RADAR’s ability to penetrate materials and AI’s advanced data processing techniques. This chapter examines how AI can be applied to RADAR data for tracking changes in infrastructure, including roads, bridges, and underground facilities, and identifying risks related to ground subsidence. The discussion includes AI-driven methodologies for structural assessment, urban expansion analysis, and predictive infrastructure maintenance. Case studies illustrate the practical applications of this technology in smart city initiatives, disaster management, and predictive maintenance. The chapter also addresses key challenges in applying AI for infrastructure monitoring, including data accuracy, empirical limitations, and ethical concerns. Finally, the future potential of AI in shaping smart, sustainable cities is discussed.
Abstract
This chapter explores how artificial intelligence (AI) drives a paradigm shift in RADAR-based remote sensing for urban infrastructureurban infrastructure monitoring. Urban monitoring has shifted toward a predictive and proactive approach by harnessing RADAR’s ability to penetrate materials and AI’s advanced data processing techniques. This chapter examines how AI can be applied to RADAR data for tracking changes in infrastructure, including roads, bridges, and underground facilities, and identifying risks related to ground subsidence. The discussion includes AI-driven methodologies for structural assessment, urban expansion analysis, and predictive infrastructure maintenance. Case studies illustrate the practical applications of this technology in smart city initiatives, disaster management, and predictive maintenance. The chapter also addresses key challenges in applying AI for infrastructure monitoring, including data accuracy, empirical limitations, and ethical concerns. Finally, the future potential of AI in shaping smart, sustainable cities is discussed.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- Integrating Sentinel-1 satellite data with machine learning for land use classification 1
- A systematic review of deep learning techniques in microwave remote sensing: challenges, applications, and future directions 17
- Fundamentals of active and passive microwave remote sensing: principles and applications 31
- Comprehensive overview of active and passive microwave remote sensing satellite sensors 55
- Essentials of RADAR remote sensing and AI integration 73
- Fusion of scatterometer and optical remote sensing: enhanced classification and change detection 91
- AI-powered urban infrastructure monitoring using RADAR-based remote sensing 103
- Fusion of the optical and microwave images for cloud removal 123
- Integrating AI in RADAR remote sensing: enhancing data processing, interpretation, and decision-making 141
- Revolutionizing precision agriculture: the synergy of RADAR, Internet of things (IoT), and satellite technology 155
- Integrating AI with RADAR remote sensing: applications in disaster mitigation, defense, and climate change 171
- Computational techniques in RADAR remote sensing from a machine and deep learning perspective 189
- Deep learning-based water body segmentation in SAR imagery: enhancing accuracy with CNN-U-Net and EfficientNet 205
- Artificial intelligence in RADAR remote sensing: advances, challenges, and future prospects 215
- Revolutionizing agricultural and environmental analytics with synthetic aperture radar (SAR): innovations, challenges, and future directions 229
- Editors’ biographies
- Index 247
