In recent years, we have witnessed numerous tragedies in both existing and newly constructed public infrastructure caused by the invasion of foreign objects, resulting in damages and disasters in railway construction. To address these challenges, our team collaborated with Sinotech Engineering Consultants Co., Ltd. and National Taipei University of Technology to develop and introduce Light Detection and Ranging (LiDAR) technology as a novel application in disaster prevention. Through real-time and comprehensive environmental scanning, our system safeguards Taiwan's transportation tracks and hilly areas 24/7, ensuring the safety of residents' homes.
Given Taiwan's location at the tectonic plate boundary with frequent earthquakes and exposure to typhoons and heavy rainfall due to its monsoon climate, natural slopes are susceptible to damage from rain, earthquakes, or human excavation.
Additionally, the protective facilities around railways and the aging electrical infrastructure have gradually become outdated over time. Regular inspections of slopes and tracks throughout the island have become challenging, making it difficult to detect and prevent potential disasters promptly.
To prevent the escalation of disasters and provide early warnings, we believe monitoring potential displacement locations is a crucial aspect of disaster prevention.
Our team highlights that the existing monitoring methods primarily rely on single-point and mobile measurements, making it challenging to present a comprehensive overview and potentially introducing significant errors.
Moreover, the accumulation of large datasets lacks systematic organization, resulting in lower analysis efficiency and limited overall identification effectiveness. Therefore, we specifically opted for LiDAR scanning devices combined with Artificial Intelligence of Things (AIoT) technology. This approach not only provides three-dimensional scanning for information visualization but also controls errors through fixed-point measurements.
Finally, we simplify massive data and promptly upload it to cloud servers. Upon detecting abnormal changes, our developed AI foreign object intrusion algorithm automatically analyzes the situation, achieving real-time monitoring and warning capabilities.
Our goal is to enhance overall disaster prevention effectiveness, ensuring the sustainable development of public infrastructure and the safety of people's lives and properties.
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