Remote Sensing Analysis of the LIDAR Drone Mapping System for Detecting Damages to Buildings, Roads, and Bridges Using the Faster CNN Method

The unmanned aerial vehicles are used with LIDAR technology and the CNN method to detect damages to roads, buildings, and bridges. The Light detection and ranging (LIDAR) is used for mapping and capturing the damage to roads and buildings, and it is a 3D mapping. The convolutional neural network (CNN) method and deep learning method are used to properly research the damaged areas and depend on low- to high-level pattern detection. It is used in visual detection and shows consistently superior accuracy for spectrogram classifications. It collects the data from damaged areas and gives the information to the device. Here, the instructions are designed in Python. We use multisensory to detect the cracks and pits, and the damaged places will be detected using sensors and sent as a pronouncement. The images are captured by the LIDAR and processed according to the instructions given by the build programming language. It is used to reduce work time and make it highly efficient. It can detect the damages automatically on high buildings, bridges, and roads. It is mostly used in civil departments. The experimental results shows that the proposed model attained the maximum accuracy of 95.88%.