scholarly journals Spatial ETL for 3D Building Modelling Based on Unmanned Aerial Vehicle Data in Semi-Urban Areas

2020 ◽  
Vol 12 (12) ◽  
pp. 1972 ◽  
Author(s):  
Urška Drešček ◽  
Mojca Kosmatin Fras ◽  
Jernej Tekavec ◽  
Anka Lisec
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Process Model ◽  
Laser Scanning ◽  
3D City Models ◽  
Aerial Vehicle ◽  
Spatial Etl ◽  
City Models

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial data for 3D modelling. In our data-driven approach, we use a spatial ETL solution to reconstruct a 3D building model from a dense image matching point cloud which was obtained beforehand from UAV imagery. The results are 3D building models in a semantic vector format consistent with the OGC CityGML standard, Level of Detail 2 (LOD2). The approach has been tested on selected buildings in a simple semi-urban area. We conclude that spatial ETL solutions can be efficiently used for 3D building modelling from UAV data, where the data process model developed allows the developer to easily control and manipulate each processing step.

Retrieving individual tree heights from a point cloud generated with optical imagery from an unmanned aerial vehicle (UAV)

2020 ◽  
Vol 50 (10) ◽  
pp. 1012-1024
Author(s):  
Meimei Wang ◽  
Jiayuan Lin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Height ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Surface Model ◽  
Individual Tree ◽  
Aerial Vehicle ◽  
The Mean

Individual tree height (ITH) is one of the most important vertical structure parameters of a forest. Field measurement and laser scanning are very expensive for large forests. In this paper, we propose a cost-effective method to acquire ITHs in a forest using the optical overlapping images captured by an unmanned aerial vehicle (UAV). The data sets, including a point cloud, a digital surface model (DSM), and a digital orthorectified map (DOM), were produced from the UAV imagery. The canopy height model (CHM) was obtained by subtracting the digital elevation model (DEM) from the DSM removed of low vegetation. Object-based image analysis was used to extract individual tree crowns (ITCs) from the DOM, and ITHs were initially extracted by overlaying ITC outlines on the CHM. As the extracted ITHs were generally slightly shorter than the measured ITHs, a linear relationship was established between them. The final ITHs of the test site were retrieved by inputting extracted ITHs into the linear regression model. As a result, the coefficient of determination (R2), the root mean square error (RMSE), the mean absolute error (MAE), and the mean relative error (MRE) of the retrieved ITHs against the measured ITHs were 0.92, 1.08 m, 0.76 m, and 0.08, respectively.

scholarly journals UNMANNED AERIAL VEHICLE LASER SCANNING FOR EROSION MONITORING IN ALPINE GRASSLAND

2019 ◽  
Vol IV-2/W5 ◽  
pp. 405-412 ◽  
Author(s):  
A. Mayr ◽  
M. Bremer ◽  
M. Rutzinger ◽  
C. Geitner
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Test Site ◽  
Satellite System ◽  
Alpine Grassland ◽  
Proof Of Concept ◽  
Aerial Vehicle ◽  
Geomorphological Changes ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> With this contribution we assess the potential of unmanned aerial vehicle (UAV) based laser scanning for monitoring shallow erosion in Alpine grassland. A 3D point cloud has been acquired by unmanned aerial vehicle laser scanning (ULS) at a test site in the subalpine/alpine elevation zone of the Dolomites (South Tyrol, Italy). To assess its accuracy, this point cloud is compared with (i) differential global navigation satellite system (GNSS) reference measurements and (ii) a terrestrial laser scanning (TLS) point cloud. The ULS point cloud and an airborne laser scanning (ALS) point cloud are rasterized into digital surface models (DSMs) and, as a proof-of-concept for erosion quantification, we calculate the elevation difference between the ULS DSM from 2018 and the ALS DSM from 2010. For contiguous spatial objects of elevation change, the volumetric difference is calculated and a land cover class (<i>bare earth</i>, <i>grassland</i>, <i>trees</i>), derived from the ULS reflectance and RGB colour, is assigned to each change object. In this test, the accuracy and density of the ALS point cloud is mainly limiting the detection of geomorphological changes. Nevertheless, the plausibility of the results is confirmed by geomorphological interpretation and documentation in the field. A total eroded volume of 672&amp;thinsp;m<sup>3</sup> is estimated for the test site (48&amp;thinsp;ha). Such volumetric estimates of erosion over multiple years are a key information for improving sustainable soil management. Based on this proof-of-concept and the accuracy analysis, we conclude that repeated ULS campaigns are a well-suited tool for erosion monitoring in Alpine grassland.</p>

scholarly journals Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

2020 ◽  
Vol 13 (1) ◽  
pp. 24
Author(s):  
Yuanshuo Hao ◽  
Faris Rafi Almay Widagdo ◽  
Xin Liu ◽  
Ying Quan ◽  
Lihu Dong ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Random Effects ◽  
Forest Inventory ◽  
Point Cloud ◽  
Laser Scanning ◽  
Small Scale ◽  
Individual Tree ◽  
Scanning Angle ◽  
Tree Diameter ◽  
Aerial Vehicle

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

scholarly journals COMPARISON AND TIME SERIES ANALYSIS OF LANDSLIDE DISPLACEMENT MAPPED BY AIRBORNE, TERRESTRIAL AND UNMANNED AERIAL VEHICLE BASED PLATFORMS

2019 ◽  
Vol IV-2/W5 ◽  
pp. 421-428 ◽  
Author(s):  
J. Pfeiffer ◽  
T. Zieher ◽  
M. Rutzinger ◽  
M. Bremer ◽  
V. Wichmann
Keyword(s):  
Time Series ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Image Correlation ◽  
Landslide Monitoring ◽  
Displacement Vectors ◽  
Aerial Vehicle ◽  
Displacement Patterns

<p><strong>Abstract.</strong> Slow moving deep-seated gravitational slope deformations are threatening infrastructure and economic wellbeing in mountainous areas. Accelerating landslides may end up in a catastrophic slope failure in terms of rapid rock avalanches. Continuous landslide monitoring enables the identification of critical acceleration thresholds, which are required in natural hazard management. Among many existing monitoring methods, laser scanning is a cost effective method providing 3D data for deriving three dimensional and areawide displacement vectors at certain morphological structures travelling on top of the landslide. Comparing displacements between selected observation periods allows the spatial interpretation of landslide acceleration or deceleration. This contribution presents five laser scanning datasets of the active Reissenschuh landslide (Tyrol, Austria) acquired by airborne laser scanning (ALS), terrestrial laser scanning (TLS) and Unmanned aerial vehicle Laser Scanning (ULS) sensors. Three observation periods with acquisition dates between 2008 and 2018 are used to derive area-wide displacement vectors. To ensure a most suitable displacement derivation between ALS, TLS and ULS platforms, an analysis investigating point cloud features within varying search radii is carried out, in order to identify a neighbourhood where common surfaces are represented platform independent or differences between the platforms are minimized. Consequent displacement vector estimation is done by ICP-Matching using morphological structures within the high resolution TLS and ULS point cloud. Displacements from the lower resolution ALS point cloud and TLS point cloud were determined using a modified version of the well-known image correlation (IMCORR) method working with point cloud derived shaded relief images combined with digital terrain models (DTM). The interplatform compatible analyses of the multi-temporal laser scanning data allows to quantify the area-wide displacement patterns of the landslide. Furthermore, changes of these displacement patterns over time are assessed area-wide. Spatially varying areas of landslide acceleration and deceleration in the order of &amp;plusmn;15&amp;thinsp;cm&amp;thinsp;a<sup>&amp;minus;1</sup> between 2008 and 2017 and an area wide acceleration of up to 20&amp;thinsp;cm&amp;thinsp;a<sup>&amp;minus;1</sup> between 2016 and 2018 are identified. Continuing the existing time series with future ULS acquisitions may enable a more complete and detailed displacement monitoring using entirely represented objects within the point clouds.</p>

scholarly journals UNLOCKING POINT CLOUD POTENTIAL: FUSING MLS POINT CLOUDS WITH SEMANTIC 3D BUILDING MODELS WHILE CONSIDERING UNCERTAINTY

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 45-52
Author(s):  
O. Wysocki ◽  
Y. Xu ◽  
U. Stilla
Keyword(s):  
Confidence Interval ◽  
Bayesian Network ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Dynamic Adjustment ◽  
3D City Models ◽  
Building Models ◽  
City Models

Abstract. Throughout the years, semantic 3D city models have been created to depict 3D spatial phenomenon. Recently, an increasing number of mobile laser scanning (MLS) units yield terrestrial point clouds at an unprecedented level. Both dataset types often depict the same 3D spatial phenomenon differently, thus their fusion should increase the quality of the captured 3D spatial phenomenon. Yet, each dataset has modality-dependent uncertainties that hinder their immediate fusion. Therefore, we present a method for fusing MLS point clouds with semantic 3D building models while considering uncertainty issues. Specifically, we show MLS point clouds coregistration with semantic 3D building models based on expert confidence in evaluated metadata quantified by confidence interval (CI). This step leads to the dynamic adjustment of the CI, which is used to delineate matching bounds for both datasets. Both coregistration and matching steps serve as priors for a Bayesian network (BayNet) that performs application-dependent identity estimation. The BayNet propagates uncertainties and beliefs throughout the process to estimate end probabilities for confirmed, unmodeled, and other city objects. We conducted promising preliminary experiments on urban MLS and CityGML datasets. Our strategy sets up a framework for the fusion of MLS point clouds and semantic 3D building models. This framework aids the challenging parallel usage of such datasets in applications such as façade refinement or change detection. To further support this process, we open-sourced our implementation.

scholarly journals Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1228
Author(s):  
Ting On Chan ◽  
Linyuan Xia ◽  
Yimin Chen ◽  
Wei Lang ◽  
Tingting Chen ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Unmanned Aerial Vehicle ◽  
Point Cloud ◽  
Geometric Model ◽  
Digital Camera ◽  
Point Clouds ◽  
Symmetry Analysis ◽  
3D Point Clouds ◽  
Transmission Towers ◽  
Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

scholarly journals Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry

2021 ◽  
Vol 13 (15) ◽  
pp. 2885
Author(s):  
Mei Li ◽  
Zengyuan Li ◽  
Qingwang Liu ◽  
Erxue Chen
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Laser Scanning ◽  
Forest Stand ◽  
Coniferous Plantation ◽  
Point Density ◽  
Stereo Photogrammetry ◽  
Aerial Vehicle ◽  
Stand Height ◽  
Median Height

Plantation forests play a critical role in forest products and ecosystems. Unmanned aerial vehicle (UAV) remote sensing has become a promising technology in forest related applications. The stand heights will reflect the growth and competition of individual trees in plantation. UAV laser scanning (ULS) and UAV stereo photogrammetry (USP) can both be used to estimate stand heights using different algorithms. Thus, this study aimed to deeply explore the variations of four kinds of stand heights including mean height, Lorey’s height, dominated height, and median height of coniferous plantations using different models based on ULS and USP data. In addition, the impacts of thinned point density of 30 pts to 10 pts, 5 pts, 1 pts, and 0.8 pts/m2 were also analyzed. Forest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation. The results showed that the prediction accuracy of the stand heights using metrics from USP was basically as good as that of ULS. Lorey’s height had the highest prediction accuracy, followed by dominated height, mean height, and median height. The correlation between height percentiles metrics from ULS and USP increased with the increased height. Different stand heights had their corresponding best height percentiles as variables based on stand height characteristics. Furthermore, canopy height model (CHM)-based metrics performed slightly better than normalized point cloud (NPC)-based metrics. The USP was not able to extract exact terrain information in a continuous coniferous plantation for forest canopy cover (CC) over 0.49. The combination of USP and terrain from ULS can be used to estimate forest stand heights with high accuracy. In addition, the estimation accuracy of each forest stand height was slightly affected by point density, which can also be ignored.

scholarly journals ENHANCEMENT OF GENERIC BUILDING MODELS BY RECOGNITION AND ENFORCEMENT OF GEOMETRIC CONSTRAINTS

2016 ◽  
Vol III-3 ◽  
pp. 333-338 ◽  
Author(s):  
J. Meidow ◽  
H. Hammer ◽  
M. Pohl ◽  
D. Bulatov
Keyword(s):  
Laser Scanning ◽  
Initial Point ◽  
Real Data ◽  
Boundary Representation ◽  
Geometric Constraints ◽  
Data Set ◽  
3D City Models ◽  
Building Models ◽  
Recognition And Enforcement ◽  
City Models

Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do not feature expected structures such as orthogonality or parallelism. The recognition and enforcement of man-made structures within model instances is one way to enhance 3D city models. Since the reconstructions are derived from uncertain and imprecise data, crisp relations such as orthogonality or parallelism are rarely satisfied exactly. Furthermore, the uncertainty of geometric entities is usually not specified in 3D city models. Therefore, we propose a point sampling which simulates the initial point cloud acquisition by airborne laser scanning and provides estimates for the uncertainties. We present a complete workflow for recognition and enforcement of man-made structures in a given boundary representation. The recognition is performed by hypothesis testing and the enforcement of the detected constraints by a global adjustment of all bounding faces. Since the adjustment changes not only the geometry but also the topology of faces, we obtain improved building models which feature regular structures and a potentially reduced complexity. The feasibility and the usability of the approach are demonstrated with a real data set.

scholarly journals SEMI-AUTOMATED APPROACH FOR MAPPING URBAN TREES FROM INTEGRATED AERIAL LiDAR POINT CLOUD AND DIGITAL IMAGERY DATASETS

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 127-134 ◽  
Author(s):  
M. A. Dogon-Yaro ◽  
P. Kumar ◽  
A. Abdul Rahman ◽  
G. Buyuksalih
Keyword(s):  
Digital Image ◽  
Spatial Data ◽  
Urban Areas ◽  
Point Cloud ◽  
Structural Complexity ◽  
Urban Ecosystems ◽  
Field Work ◽  
Urban Trees ◽  
Decision Makers ◽  
Image Datasets

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraints, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This paper presented a workflow about semi-automated approach for extracting urban trees from integrated processing of airborne based LiDAR point cloud and multispectral digital image datasets over Istanbul city of Turkey. The paper reveals that the integrated datasets is a suitable technology and viable source of information for urban trees management. As a conclusion, therefore, the extracted information provides a snapshot about location, composition and extent of trees in the study area useful to city planners and other decision makers in order to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.

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