Carlos Cabo

Comparing Terrestrial Laser Scanning (TLS) and Wearable Laser Scanning (WLS) for Individual Tree Modeling at Plot Level

This study presents a comparison between the use of wearable laser scanning (WLS) and terrestrial laser scanning (TLS) devices for automatic tree detection with an estimation of two dendrometric variables: diameter at breast height (DBH) and total tree height (TH). Operative processes for data collection and automatic forest inventory are described in detail. The approach used is based on the clustering of points belonging to each individual tree, the isolation of the trunks, the iterative fitting of circles for the DBH calculation and the computation of the TH of each tree. TLS and WLS point clouds were compared by the statistical analysis of both estimated forest dendrometric parameters and the possible presence of bias. Results show that the apparent differences in point density and relative precision between both 3D forest models do not affect tree detection and DBH estimation. Nevertheless, tree height estimation using WLS appears to be affected by the limited scanning range of the WLS used in this study. TH estimations for trees below a certain height are equivalent using WLS or TLS, whereas TH of taller trees is clearly underestimated using WLS.

Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning

Abstract This study presents an automatic method to identify tree stems, and estimate tree heights and diameters from terrestrial laser scanning (TLS) data. The method is based on the isolation and vertical continuity of the stems. First, a height-normalized version of the point cloud is created. From this, stems are individualized, an iterative process is applied to the points at breast height for estimating diameters, and tree heights are calculated after denoising and clustering the points of each tree. The method was tested in three different sites. All the elements detected as trees were actual trees, and more than 99% of the trees in the plots were detected. Root mean square error (RMSE) of the estimated diameters at breast height (DBH) ranged from 0.8 to 1.3 cm in the test plots, and total tree height (TH) RMSE ranged from 0.3 to 0.7 m. In the cases studied, the algorithm showed robustness to the presence of steep or irregular terrain, the presence of low vegetation and artifacts at breast height, the indistinct use of individual or multiple scans, and tree density in the plot.

Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data

Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%.

Detection of human vital signs in hazardous environments by means of video magnification

In cases of natural disasters, epidemics or even in dangerous situations like an act of terrorism, battle fields, a shooting or a mountain accident, finding survivors is a challenge. In these kind of situations it is sometimes critical to know if a person has vital signs or not, without the need to be in contact with the victim, thus avoiding jeopardizing the lives of the rescue workers. In this work, we propose the use of video magnification techniques to detect small movements in human bodies due to breathing that are invisible to the naked eye. Two different video magnification techniques, intensity-based and phase-based, were tested. The utility of these techniques to detect people who are alive but injured in risk situations was verified by simulating a scene with three people involved in an accident. Several factors such as camera stability, distance to the object, light conditions, magnification factor or computing time were analyzed. The results obtained were quite positive for both techniques, intensity-based method proving more adequate if the interest is in almost instant results whereas the phase-based method is more appropriate if processing time is not so relevant but the degree of magnification without excessive image noise.

Detecting imperceptible movements in structures by means of video magnification

The naked eye is not able to perceive very slow movements such as those occurring in certain structures under external forces. This might be the case of metallic or concrete bridges, tower cranes or steel beams. However, sometimes it is of interest to view such movements, since they can provide useful information regarding the mechanical state of those structures. In this work, we analyze the utility of video magnification to detect imperceptible movements in several types of structures. First, laboratory experiments were conducted to validate the method. Then, two different tests were carried out on real structures: one on a water slide and another on a tower crane. The results obtained allow us to conclude that image cross-correlation and video magnification is indeed a promising low-cost technique for structure health monitoring.

Automatic road edge detection from Mobile Laser Scanning (MLS)

In this article we present an algorithm for automatic road edge detection from MLS (Mobile Laser Scanning) data. The method takes advantage of linear structures derived from MLS point clouds. These lines are extracted from the point cloud and grouped following geometric restrictions. Then, the outlines of the groups are extracted as road edges. Finally, a moving window filter is applied to those points in order to remove outliers and delineate the road edge. The method was tested on an 800m stretch of road, and the results were checked through visual inspection. Correctness and completeness were 99.1% and 97.5%, respectively.