AI and data-driven methods, particularly machine and deep learning models, have revolutionized image processing tasks such as object detection and segmentation, as well as inverse sensing tasks such as atmospheric condition monitoring. These models enable highly accurate predictions from large, labelled datasets. However, one of the key challenges with current AI systems is their opacity. While they deliver precise results, they often fail to provide clear insights into the underlying mechanisms driving these predictions. The lack of interpretability, especially in the context of sensing data, complicates the understanding of how internal features are represented and utilized. This issue is particularly pronounced in smart environment applications, where artificial intelligence techniques are increasingly employed, yet their internal workings remain largely opaque.In this edited book, the contributors explore the potential of machine intelligence, deep learning and sensing technologies, with a focus on their integration and applications. They present new methods, cutting-edge technologies and practical solutions to real-world challenges. The goal is to help readers better grasp the concepts necessary for designing and deploying the next generation of smarter and more sustainable systems and environments.The Convergence of AI, IoT, Smart Sensors and Remote Sensing for Sustainable Smart Environment Applications is a useful reference for researchers, engineers and scientists in academia and industry, as well as advanced students in the fields of data science and security, AI and networking, communication engineering, smart sensing, remote sensing and automation. It will also be of interest to system designers and developers of AI and sensing technologies for smart environment applications.
