Road extraction from Light Detection and Ranging (LiDAR) has become a hot topic over recent years. Nevertheless, it is still challenging to perform this task in a fully automatic way. Experiments are often carried out over small datasets with a focus on urban areas and it is unclear how these methods perform in less urbanized sites. Furthermore, some methods require the manual input of critical parameters, such as an intensity threshold. Aiming to address these issues, this paper proposes a method for the automatic extraction of road points suitable for different landscapes. Road points are identified using pipeline filtering based on a set of constraints defined on the intensity, curvature, local density, and area. We focus especially on the intensity constraint, as it is the key factor to distinguish between road and ground points. The optimal intensity threshold is established automatically by an improved version of the skewness balancing algorithm. Evaluation was conducted on ten study sites with different degrees of urbanization. Road points were successfully extracted in all of them with an overall completeness of 93%, a correctness of 83%, and a quality of 78%. These results are competitive with the state-of-the-art.

Powerline inspection and modelization using airborne light detection and ranging (LiDAR) data have been widely studied through the years. However, to the best of our knowledge, the proposed methods rely on intentional flights carried out along the high-voltage powerline. Thus, the state-of-the-art studies focus on detecting and characterizing a single powerline whose presence and location are known beforehand. We propose a method to detect and model powerlines of any voltage from airborne LiDAR point clouds not necessarily acquired for this purpose. Also, the method is suitable to be applied to those point clouds whose density is usually lower than that obtained using specific purpose flights over the powerlines. Our solution starts filtering out most of the points that do not belong to electric conductors. Then, the Hough transform is used to detect straight lines. Its output is then used to cluster the electric conductors. Also, we propose a solution to bypass a common issue regarding the nonmaxima suppression often used in object detection algorithms. Furthermore, a robust method for clustering conductors sharing the same vertical plane is presented, being able to return good results even in the absence of parts of any electrical conductor. The algorithm is tested in several datasets containing high-voltage powerlines and others, comprising mid- and low-voltage electric conductors. Finally, a study of the computational performance shows that the algorithm can efficiently take advantage of manycore systems, which is essential to determine the feasibility of our approach on massive LiDAR point clouds.

In this work we present a novel strategy for the simultaneous design and training of multilayer discrete-time cellular neural networks. This methodology is applied to the detection of surface-laid antipersonnel mines in infrared imaging. The procedure is based on the application of genetic algorithms for both network design and learning task.

Over the last two decades, a wide range of applications have been developed from Light Detection and Ranging (LiDAR) point clouds. Most LiDAR-derived products require the distinction between ground and non-ground points. Because of this, ground filtering its being one of the most studied topics in the literature and robust methods are nowadays available. However, these methods have been designed to work with offline data and they are generally not well suited for real-time scenarios. Aiming to address this issue, this paper proposes an efficient method for ground filtering of airborne LiDAR data based on scan-line processing. In our proposal, an iterative 1-D spline interpolation is performed in each scan line sequentially. The final spline knots of a scan line are taken into account for the next scan line, so that valuable 2-D information is also considered without compromising computational efficiency. Points are labelled into ground and non-ground by analysing their residuals to the final spline. When tested against synthetic ground truth, the method yields a mean kappa value of 88.59% and a mean total error of 0.50%. Experiments with real data also show satisfactory results under visual inspection. Performance tests on a workstation show that the method can process up to 1 million points per second. The original implementation was ported into a low-cost development board to demonstrate its feasibility to run in embedded systems, where throughput was improved by using programmable logic hardware acceleration. Analysis shows that real-time filtering is possible in a high-end board prototype, as it can process the amount of points per second that current lightweight scanners acquire with low-energy consumption.

Reconfigurable hardware circuits, such as field-programmable gate arrays, have gained popularity in the high-performance computing (HPC) community in recent years. Nevertheless, their real contribution to accelerating HPC workloads is unclear in both potential and extent.