Automatic Threat Detection for Historic Buildings in Dark Places Based on the Modified OptD Method

Abstract

Historic buildings, due to their architectural, cultural, and historical value, are the subject of preservation and conservatory works. Such operations are preceded by an inventory of the object. One of the tools that can be applied for such purposes is Light Detection and Ranging (LiDAR). This technology provides information about the position, reflection, and intensity values of individual points; thus, it allows for the creation of a realistic visualization of the entire scanned object. Due to the fact that LiDAR allows one to ‘see’ and extract information about the structure of an object without the need for external lighting or daylight, it can be a reliable and very convenient tool for data analysis for improving safety and avoiding disasters. The main goal of this paper is to present an approach of automatic wall defect detection in unlit sites by means of a modified Optimum Dataset (OptD) method. In this study, the results of Terrestrial Laser Scanning (TLS) measurements conducted in two historic buildings in rooms without daylight are presented. One location was in the basement of the ruins of a medieval tower located in Dobre Miasto, Poland, and the second was in the basement of a century-old building located at the University of Warmia and Mazury in Olsztyn, Poland. The measurements were performed by means of a Leica C-10 scanner. The acquired dataset of x, y, z, and intensity was processed by the OptD method. The OptD operates in such a way that within the area of interest where surfaces are imperfect (e.g., due to cracks and cavities), more points are preserved, while at homogeneous surfaces (areas of low interest), more points are removed (redundant information). The OptD algorithm was additionally modified by introducing options to detect and segment defects on a scale from 0 to 3 (0—harmless, 1—to the inventory, 2—requiring repair, 3—dangerous). The survey results obtained proved the high effectiveness of the modified OptD method in the detection and segmentation of the wall defects. The values of area of changes were calculated. The obtained information about the size of the change can be used to estimate the costs of repair, renovation, and reconstruction.

Application of the Msplit Estimation Method in the Detection and Dimensioning of the Displacement of Adjacent Planes

“…Based on the assumption that the functional model is split into more than two competing models, the Msplit(q) estimation algorithm can be written in the following form described by Equation (8). The model is then fitted to q competing functional models (l = 1, ..., q).…”

“…Nowadays, the use of terrestrial laser scanners (TLSs) is gaining more and more attention and application as a fast, accurate, and non-destructive measurement method (NDT) [6][7][8].…”

Classification and Segmentation of Mining Area Objects in Large-Scale Spares Lidar Point Cloud Using a Novel Rotated Density Network

“…The Light Detection and Ranging (LiDAR) has been widely used for its fast, convenient, and non-contact measurement advantages [1][2][3]. The point cloud is the direct output from a LiDAR sensor, which is a detailed expression of all objects detected by the sensor.…”

“…Commonly the point-density is given for one cubic meter (pts/m 3 ). For engineering survey, high, medium, low, and sparse density point cloud is defined as point-density < 2 pts/m 3 , (2,7] pts/m 3 , (7,10] pts/m 3 and >10 pts/m 3 , respectively. In order to reduce the computational costs and improve efficiency, many researchers applied the sparse point cloud for classification or segmentation, by using deep learning technology [18,19].…”

Determination of Terrain Profile from TLS Data by Applying Msplit Estimation

“…Depending on the type of platform, there is airborne laser scanning (ALS), terrestrial laser scanning (TLS), satellite laser scanning (SLS) and mobile laser scanning (MLS) [1,2]. All types of laser scanning have a wide range of applications, e.g., terrain surface and vegetation cover measurements and digital terrain model (DTM) generation [3][4][5][6][7], building detection and their condition diagnosis [8][9][10][11][12], displacement detection [13][14][15], modeling of cultural heritage sites or object structures [2,13,16], registration roads, railways or power lines [17,18], monitoring coastal zones [19], mining damages and ground disasters [20,21].…”