The Slope Monitoring Using Embedded System with Optical-Thermal Image Fusion and Machine Learning

Taiwan is at the junction of tectonic plates, with high mountainous terrain and abundant rainfall due to the rich moisture of the subtropical monsoon zone, which makes the already dangerous slopes even more unstable. By applying the digital camera measurement approach, with its moderate installation cost, high security, and mobility, it is possible to capture and monitor the hillside over an extended period, guaranteeing its stability. Photogrammetry is a recent technology that has gained popularity in many fields. Image analysis, achieved by capturing photographs, is a growing trend. It offers a cost-effective way of transmitting information in automated Internet of Things (IoT) systems, reducing human and financial resources. Therefore, this study aims to utilize close-range photogrammetry to analyze both 2D and 3D image data. Besides, this study encompasses thermal images, enabling researchers to evaluate observed objects' temperature differences quickly. Thus, this study aims to superimpose temperature information from thermal images onto optical images, in order to analyze slope information. It focuses on creating on-site cameras that use both optical and thermal imaging, building a three-dimensional point cloud using optical systems, investigating image displacements, and tracking unstable areas using thermal imaging. Additionally, the research explores the potential of AI interpretation to aid in this process. The laboratory and the preliminary field test support the feasibility of the proposed system and interpretation methods.