LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Abstract

The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images

“…Recently, studies in literature have moved towards data integration in order to improve the accuracy of urban LULC classification [15,16]. Data integration refers to the integration of information from various sources or sensors, such as the integration of LiDAR and optical data, for instance [17][18][19]. Nevertheless, even these techniques used in LULC classification present some issues since they are extremely affected by the environmental changes, like destruction of essential wetlands, uncontrolled urban development, haphazard, loss of prime agricultural lands, deteriorating environmental quality, and also by other factors, like cloud cover and regional fog errors.…”

“…Nevertheless, even these techniques used in LULC classification present some issues since they are extremely affected by the environmental changes, like destruction of essential wetlands, uncontrolled urban development, haphazard, loss of prime agricultural lands, deteriorating environmental quality, and also by other factors, like cloud cover and regional fog errors. Moreover, traditional remote-sensing imagery processing offers some other concerns: the noise associated with the image, the orientation of features in the images and the maintenance of a large volume of data [19,20]. Several methodologies have been developed by the researchers to address those issues faced in LULC classification.…”

A Case Study of Deformation Measurements of Istanbul Land Walls via Terrestrial Laser Scanning

“…Using these models, the angles of inclinations were estimated. In [5], two case studies based on the use of 2D and 3D LiDARs showed that LiDAR allows the extraction of accurate building features and accomplishes in monitoring and analysis of relevant areas. The combination of TLS and photogrammetry for structural monitoring are shown in [6].…”

Industrial Robot Control by Means of Gestures and Voice Commands in Off-Line and On-Line Mode

“…This approach allows the scanning of large objects on the one hand, but on the other it is flexible and easily reprogrammable. 3D scanners can also be used to scan larger areas, such as robotic production cells or even entire buildings [30,31]. The limitations of the sensor's design must be taken into account, as the use of the IR detector requires appropriate environmental conditions to eliminate interference.…”