Parameter-free ground filtering of LiDAR data for automatic DTM generation

Mongus, Domen; Žalik, Borut

doi:10.1016/j.isprsjprs.2011.10.002

Cited by 215 publications

(179 citation statements)

References 18 publications

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“…isprs.org/wg3/) and compare the filtering accuracy of SGF with those of eight classical filtering methods [2], parameter-free algorithm [1], Terrasolid TerraScan, and BCD. The ISPRS dataset is composed of 15 samples with different terrain features and point spacing.…”

Section: Quality Assessment On Isprs Test Data Setmentioning

confidence: 99%

“…We also compare our results with the state-of-the-art parameter-free algorithm [1] and the most popular commercial software TerraSolid TerraScan. TerraScan uses the TIN-based filtering method.…”

Section: Quality Assessment On Isprs Test Data Setmentioning

confidence: 99%

“…LiDAR is capable of directly acquiring high-accuracy 3D coordinates of discrete points on the earth's surface. The extraction of Digital Terrain Models (DTM) from airborne LiDAR point cloud has been an attractive research topic [1]. As both ground and non-ground objects (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…However, discontinuities often happen on steep terrains, such as terraced fields, scarps, and steep forested areas [3]. Therefore, ground with discontinuities may be filtered out, which may cause a decline in accuracy [1]. To address such problems, global-based methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…To address such problems, global-based methods have been developed. Thin plate spline [1] and active shape model [21] model the ground point extraction from airborne LiDAR as a global optimization problem. The authors of [22,23] use the Markov Random Field (MRF) to label each point with different classes.…”

Section: Introductionmentioning

confidence: 99%

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Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Pang

et al. 2015

Remote Sensing

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Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF). The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second. OPEN ACCESSRemote Sens. 2015, 7 10997

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Section: Quality Assessment On Isprs Test Data Setmentioning

confidence: 99%

Section: Quality Assessment On Isprs Test Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Pang

et al. 2015

Remote Sensing

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Human‐in‐the‐loop development of spatially adaptive ground point filtering pipelines—An archaeological case study

Doneus

Höfle

Kempf

et al. 2022

Archaeological Prospection

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LiDAR data have become indispensable for research in archaeology and a variety of other topographic applications. To derive products (e.g. digital terrain or feature models, individual trees, buildings), the 3D LiDAR points representing the desired objects of interest within the acquired and georeferenced point cloud need to be identified. This process is known as classification, where each individual point is assigned to an object class. In archaeological prospection, classification focuses on identifying the object class 'ground points'. These are used to interpolate digital terrain models exposing the microtopography of a terrain to be able to identify and map archaeological and palaeoenvironmental features. Setting up such classification workflows can be time-consuming and prone to information loss, especially in geographically heterogeneous landscapes. In such landscapes, one classification setting can lead to qualitatively very different results, depending on varying terrain parameters such as steepness or vegetation density. In this paper, we are focussing on a special workflow for optimal classification results in these heterogeneous environments, which integrates expert knowledge. We present a novel Pythonbased open-source software solution, which helps to optimize this process and creates a single digital terrain model by an adaptive classification based on spatial segments. The advantage of this approach for archaeology is to produce coherent digital terrain models even in geomorphologically heterogenous areas or areas with patchy vegetation. The software is also useful to study the effects of different algorithm and parameter combinations on digital terrain modelling with a focus on a practical and time-saving implementation. As the developed pipelines and all meta-information are made available with the resulting data set, classification is white boxed and consequently scientifically comprehensible and repeatable.Together with the software's ability to simplify classification workflows significantly, it will be of interest for many applications also beyond the examples shown from archaeology.

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

Easson¹,

Tavakkoli²,

Greenberg³

2019

Advances in Visual Computing

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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.

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Parameter-free ground filtering of LiDAR data for automatic DTM generation

Cited by 215 publications

References 18 publications

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Semi-Global Filtering of Airborne LiDAR Data for Fast Extraction of Digital Terrain Models

Human‐in‐the‐loop development of spatially adaptive ground point filtering pipelines—An archaeological case study

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

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