Construction of Asbestos Slate Deep-Learning Training-Data Model Based on Drone Images

Abstract

The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.

Explainable Automatic Detection of Fiber–Cement Roofs in Aerial RGB Images

“…However, they suffer from limitations in large-scale rooftop asbestos (or other materials, for that matter) identification due to the high costs and limited availability of HSI, infrared, or thermal bands [38]. As a result, even if those models are highly accurate, they may not be transferable or applicable to large areas [39]. On the other hand, even when free-access satellite images like Sentinel-2 MSI and Landsat series are available [40], their use in varied contexts is limited by spatial resolution constraints.…”