Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

Download Full-text

Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

Remote Sensing ◽

10.3390/rs11091037 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1037 ◽

Cited By ~ 12

Author(s):

Shangshu Cai ◽

Wuming Zhang ◽

Xinlian Liang ◽

Peng Wan ◽

Jianbo Qi ◽

...

Keyword(s):

Digital Terrain Model ◽

Point Clouds ◽

Parameter Tuning ◽

Airborne Lidar ◽

Lidar Data ◽

Cloth Simulation ◽

Terrain Model ◽

Adaptive Parameter ◽

Digital Terrain ◽

Airborne Lidar Data

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

Download Full-text

Computationally Efficient Method for the Generation of a Digital Terrain Model From Airborne LiDAR Data Using Connected Operators

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2013.2262996 ◽

2014 ◽

Vol 7 (1) ◽

pp. 340-351 ◽

Cited By ~ 34

Author(s):

Domen Mongus ◽

Borut Zalik

Keyword(s):

Efficient Method ◽

Digital Terrain Model ◽

Airborne Lidar ◽

Lidar Data ◽

Computationally Efficient ◽

Terrain Model ◽

Connected Operators ◽

Digital Terrain ◽

Airborne Lidar Data

Download Full-text

Digital terrain model extraction from airborne LiDAR data in complex mining area

2010 18th International Conference on Geoinformatics ◽

10.1109/geoinformatics.2010.5567781 ◽

2010 ◽

Cited By ~ 1

Author(s):

Haiyang Yu ◽

Xiaoping Lu ◽

Xiaosan Ge ◽

Gang Cheng

Keyword(s):

Digital Terrain Model ◽

Mining Area ◽

Airborne Lidar ◽

Lidar Data ◽

Model Extraction ◽

Terrain Model ◽

Digital Terrain ◽

Airborne Lidar Data

Download Full-text

3D-Modelling of Charlemagne’s Summit Canal (Southern Germany)—Merging Remote Sensing and Geoarchaeological Subsurface Data

Remote Sensing ◽

10.3390/rs11091111 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1111 ◽

Cited By ~ 3

Author(s):

Johannes Schmidt ◽

Johannes Rabiger-Völlmer ◽

Lukas Werther ◽

Ulrike Werban ◽

Peter Dietrich ◽

...

Keyword(s):

Cross Sections ◽

3D Model ◽

Digital Terrain Model ◽

Airborne Lidar ◽

Engineering Construction ◽

Terrain Model ◽

Digital Terrain ◽

Danube Catchment ◽

The Difference ◽

Airborne Lidar Data

The Early Medieval Fossa Carolina is the first hydro-engineering construction that bridges the Central European Watershed. The canal was built in 792/793 AD on order of Charlemagne and should connect the drainage systems of the Rhine-Main catchment and the Danube catchment. In this study, we show for the first time, the integration of Airborne LiDAR (Light Detection and Ranging) and geoarchaeological subsurface datasets with the aim to create a 3D-model of Charlemagne’s summit canal. We used a purged Digital Terrain Model that reflects the pre-modern topography. The geometries of buried canal cross-sections are derived from three archaeological excavations and four high-resolution direct push sensing transects. By means of extensive core data, we interpolate the trench bottom and adjacent edges along the entire canal course. As a result, we are able to create a 3D-model that reflects the maximum construction depth of the Carolingian canal and calculate an excavation volume of approx. 297,000 m3. Additionally, we compute the volume of the present dam remnants by Airborne LiDAR data. Surprisingly, the volume of the dam remnants reveals only 120,000 m3 and is much smaller than the computed Carolingian excavation volume. The difference reflects the erosion and anthropogenic overprint since the 8th century AD.

Download Full-text

High-Resolution Terrain Modeling Using Airborne LiDAR Data with Transfer Learning

Remote Sensing ◽

10.3390/rs13173448 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3448

Author(s):

Huxiong Li ◽

Weiya Ye ◽

Jun Liu ◽

Weikai Tan ◽

Saied Pirasteh ◽

...

Keyword(s):

Transfer Learning ◽

Total Error ◽

Digital Terrain Model ◽

Point Clouds ◽

Airborne Lidar ◽

Type I ◽

Type Ii ◽

Terrain Model ◽

Digital Terrain ◽

Airborne Lidar Data

This study presents a novel workflow for automated Digital Terrain Model (DTM) extraction from Airborne LiDAR point clouds based on a convolutional neural network (CNN), considering a transfer learning approach. The workflow consists of three parts: feature image generation, transfer learning using ResNet, and interpolation. First, each point is transformed into a featured image based on its elevation differences with neighboring points. Then, the feature images are classified into ground and non-ground using ImageNet pretrained ResNet models. The ground points are extracted by remapping each feature image to its corresponding points. Last, the extracted ground points are interpolated to generate a continuous elevation surface. We compared the proposed workflow with two traditional filters, namely the Progressive Morphological Filter (PMF) and the Progressive Triangulated Irregular Network Densification (PTD). Our results show that the proposed workflow establishes an advantageous DTM extraction accuracy with yields of only 0.52%, 4.84%, and 2.43% for Type I, Type II, and the total error, respectively. In comparison, Type I, Type II, and the total error for PMF are 7.82%, 11.60%, and 9.48% and for PTD 1.55%, 5.37%, and 3.22%, respectively. The root means square error (RMSE) for the 1 m resolution interpolated DTM is only 7.3 cm. Moreover, we conducted a qualitative analysis to investigate the reliability and limitations of the proposed workflow.

Download Full-text