An Automatic Digital Terrain Generation Technique for Terrestrial Sensing and Virtual Reality Applications

The identification and modeling of the terrain from point cloud data is an important component of Terrestrial Remote Sensing (TRS) applications. The main focus in terrain modeling is capturing details of complex geological features of landforms. Traditional terrain modeling approaches rely on the user to exert control over terrain features. However, relying on the user input to manually develop the digital terrain becomes intractable when considering the amount of data generated by new remote sensing systems capable of producing massive aerial and ground-based point clouds from scanned environments. This article provides a novel terrain modeling technique capable of automatically generating accurate and physically realistic Digital Terrain Models (DTM) from a variety of point cloud data. The proposed method runs efficiently on large-scale point cloud data with real-time performance over large segments of terrestrial landforms. Moreover, generated digital models are designed to effectively render within a Virtual Reality (VR) environment in real time. The paper concludes with an in-depth discussion of possible research directions and outstanding technical and scientific challenges to improve the proposed approach

DETECTION OF PLANAR POINTS FOR BUILDING EXTRACTION FROM LIDAR DATA BASED ON DIFFERENTIAL MORPHOLOGICAL AND ATTRIBUTE PROFILES

This paper considers a new method for building-extraction from LiDAR data. This method uses multi-scale levelling schema or MSLS-segmentation based on differential morphological profiles for removing non-building points from LiDAR data during the data denoising step. A new morphological algorithm is proposed for the detection of flat regions and obtaining a set of building-candidates. This binarisation step is made by using differential attribute profiles based on the sum of the second-order morphological gradients. Any distinction between flat and rough surfaces is achieved by area-opening, as applied within each attribute-zone. Thus, the detection of the flat regions is essentially based on the average gradient contained within a region, whilst avoiding subtractive filtering rule. Finally, the shapes of the flat-regions are considered during the building-recognition step. A binary shape-compactness attribute opening is used for this purpose. The efficiency of the proposed method was demonstrated on three test LiDAR datasets containing buildings of different sizes, shapes, and structures. As shown by the experiments, the average quality of the buildings-extraction was more than 95 %, with 96 % correctness, and 98 % completeness. In terms of quality, this method is comparable with TerraScan®, but both methods significantly differ when comparing correctness and completeness of the results

Rapport sur le comparatif des méthodes de détection d'arbre

In the presented study established single tree detection methods are benchmarked and investigated. In total eight airborne laser scanning (ALS) based detection methods were applied to a unique dataset originating from different regions of the Alpine Space covering different study areas, forest types and structures. The evaluation of the detection results was carried out in a clear and reproducible way by automatically matching the detection results to precise in-situ forest inventory data. Quantitative statistical parameters such as the percentages of correctly matched trees and omission and commission errors are presented. The benchmarking results are prepared in complementary levels of information, starting with the analysis based on study area as well as detection method. Additionally investigations per forest type and an overall performance of the benchmark are presented. The best matching rate was obtained for single layered coniferous forests. Trees in lower height layers were challenging for all tested methods. The overall performance shows a matching rate of 47% which is comparable to results of other benchmarks performed in the past for other forest types. The study brings new hindsight regarding the potential and limits of tree detection with ALS and underlines some key aspects regarding the choice of method when performing single tree detection for the various forest types encountered in alpine regions