ORCID Profile
0000-0001-8953-6394
Current Organisation
UNSW Australia
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Publisher: MDPI AG
Date: 18-06-2015
DOI: 10.3390/RS70608019
Publisher: MDPI AG
Date: 20-08-2014
DOI: 10.3390/RS6087762
Publisher: IEEE
Date: 2003
Publisher: Elsevier BV
Date: 03-2015
Publisher: SPIE
Date: 09-06-2011
DOI: 10.1117/12.889440
Publisher: Copernicus GmbH
Date: 13-09-2013
DOI: 10.5194/ISPRSANNALS-II-2-W1-285-2013
Abstract: Abstract. Water run-off modelling applied within urban areas requires an appropriate detailed surface model represented by a raster height grid. Accurate simulations at this scale level have to take into account small but important water barriers and flow channels given by the large-scale map definitions of buildings, street infrastructure, and other terrain objects. Thus, these 3D features have to be rasterised such that each cell represents the height of the object class as good as possible given the cell size limitations. Small grid cells will result in realistic run-off modelling but with unacceptable computation times larger grid cells with averaged height values will result in less realistic run-off modelling but fast computation times. This paper introduces a height grid generalisation approach in which the surface characteristics that most influence the water run-off flow are preserved. The first step is to create a detailed surface model (1:1.000), combining high-density laser data with a detailed topographic base map. The topographic map objects are triangulated to a set of TIN-objects by taking into account the semantics of the different map object classes. These TIN objects are then rasterised to two grids with a 0.5m cell-spacing: one grid for the object class labels and the other for the TIN-interpolated height values. The next step is to generalise both raster grids to a lower resolution using a procedure that considers the class label of each cell and that of its neighbours. The results of this approach are tested and validated by water run-off model runs for different cellspaced height grids at a pilot area in Amersfoort (the Netherlands). Two national datasets were used in this study: the large scale Topographic Base map (BGT, map scale 1:1.000), and the National height model of the Netherlands AHN2 (10 points per square meter on average). Comparison between the original AHN2 height grid and the semantically enriched and then generalised height grids shows that water barriers are better preserved with the new method. This research confirms the idea that topographical information, mainly the boundary locations and object classes, can enrich the height grid for this hydrological application.
Publisher: Copernicus GmbH
Date: 30-05-2018
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-1229-2018
Abstract: Abstract. Change detection is an essential step to locate the area where an old model should be updated. With high density and accuracy, LiDAR data is often used to create a 3D city model. However, updating LiDAR data at state or nation level often takes years. Very high resolution (VHR) images with high updating rate is therefore an option for change detection. This paper provides a novel and efficient approach to derive pixel-based building change detection between past LiDAR and new VHR images. The proposed approach aims notably at reducing false alarms of changes near edges. For this purpose, LiDAR data is used to supervise the process of finding stereo pairs and derive the changes directly. This paper proposes to derive three possible heights (so three DSMs) by exploiting planar segments from LiDAR data. Near edges, the up to three possible heights are transformed into discrete disparities. A optimal disparity is selected from a reasonable and computational efficient range centered on them. If the optimal disparity is selected, but still the stereo pair found is wrong, a change has been found. A Markov random field (MRF) with built-in edge awareness from images is designed to find optimal disparity. By segmenting the pixels into plane and edge segments, the global optimization problem is split into many local ones which makes the optimization very efficient. Using an optimization and a consecutive occlusion consistency check, the changes are derived from stereo pairs having high color difference. The algorithm is tested to find changes in an urban areas in the city of Amersfoort, the Netherlands. The two different test cases show that the algorithm is indeed efficient. The optimized disparity images have sharp edges along those of images and false alarms of changes near or on edges and occlusions are largely reduced.
Publisher: SPIE-Intl Soc Optical Eng
Date: 30-10-2017
Publisher: Informa UK Limited
Date: 12-2004
Publisher: Informa UK Limited
Date: 2000
Publisher: IEEE
Date: 06-2013
Publisher: Wiley
Date: 12-2011
Publisher: Elsevier BV
Date: 06-2017
Publisher: Copernicus GmbH
Date: 05-10-2016
DOI: 10.5194/ISPRS-ANNALS-IV-2-W1-271-2016
Abstract: Abstract. The paper presents a new algorithm to reconstruct elongated objects defined by cross sections and trajectories in gridded threedimensional models represented as voxels. Ex les of such objects are the elements of underground infrastructure in urban environments, such as pipes, conduits and tunnels. Starting from a basic methodology, which is based on distance transformations, the algorithm is extended in three ways on the basis of Voronoi datasets being produced alongside.
Publisher: IOP Publishing
Date: 18-03-2014
Publisher: Elsevier BV
Date: 04-2014
Publisher: Copernicus GmbH
Date: 29-05-2019
DOI: 10.5194/ISPRS-ANNALS-IV-2-W5-279-2019
Abstract: Abstract. The paper proposes to use voxel models of building interiors to perform indoor navigation. The algorithms can be purely geometrical, not relying on semantic information about different building elements, such as floors, walls, stairways etc. Therefore, it is possible to use voxel models from different data sources, in addition to vector-to-raster conversions. The paper demonstrates this on the basis of tree different input types: hand measurements, point clouds and images of floorplans. On the basis of these models, the paper shows how to determine the navigable space in a voxel model for a pedestrian actor, and how to compute paths from arbitrary sources to specified destinations.
Publisher: Springer Berlin Heidelberg
Date: 2005
Publisher: Copernicus GmbH
Date: 20-07-2012
DOI: 10.5194/ISPRSANNALS-I-3-161-2012
Abstract: Abstract. As lidar point clouds become larger streamed processing becomes more attractive. This paper presents a framework for the streamed segmentation of point clouds with the intention of segmenting unstructured point clouds in real-time. The framework is composed of two main components. The first component segments points within a window shifting over the point cloud. The second component stitches the segments within the windows together. In this fashion a point cloud can be streamed through these two components in sequence, thus producing a segmentation. The algorithm has been tested on airborne lidar point cloud and some results of the performance of the framework are presented.
Publisher: Informa UK Limited
Date: 1998
Publisher: IWA Publishing
Date: 10-10-2012
Abstract: Vegetation density is among the important parameters required for determination of hydrodynamic roughness over vegetated areas. High density airborne light detection and ranging (LiDAR) data offer several potentials to improve estimation of vegetation density. Available methods in estimating vegetation density based on regression models did not take into account understorey vegetation and were not tested under different forest conditions. We present a method to develop and validate a generic regression model by using simulations of airborne laser scanning. The results show that available indices failed to produce good estimation which leads to a new predictor called low points index (LP). The vegetation density of trees is estimated using the FLI-MAP 400 data based on a regression model and estimated tree diameter at breast height. Finally, vegetation density is estimated at different spatial resolutions, which is useful for the estimation of multi-resolution and spatially distributed hydrodynamic roughness.
Publisher: Copernicus GmbH
Date: 30-05-2018
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-1199-2018
Abstract: Abstract. Various kinds of urban applications require true orthophotos. True orthophoto generation requires a DSM (Digital Surface Model) to project the photo orthogonally and minimize geometric distortion due to topographic variance. DSMs are often generated from airborne laser scan data. In urban scenes, DSM data may fail to deliver sharp and straight building roof edges. This will affect the quality of the resulting orthophotos. Therefore, it is necessary to incorporate good quality building outlines as breaklines during DSM interpolation. This study proposes a data-driven approach to construct building roof outlines from LiDAR point clouds by a workflow consisting of the following steps: given roof segments, roof boundary points are extracted using a concave hull algorithm. Straight edges may be difficult to find in complex roof configurations. Therefore, two ingredients are combined. First, RanSAC corner point preselection, and second, DBSCAN-based clustering of edge points. The method is demonstrated on an area of ±1.2 km2 containing 42 buildings of different characteristics. A quality assessment shows that the proposed method is able to deliver 92 % of building lines with acceptable geometric accuracy in comparison to a building line in the base map.
Publisher: Informa UK Limited
Date: 2003
Publisher: Elsevier BV
Date: 03-2005
Publisher: MDPI AG
Date: 03-01-2019
DOI: 10.3390/RS11010072
Abstract: Up-to-date 3D city models are needed for many applications. Very-high-resolution (VHR) images with rich geometric and spectral information and a high update rate are increasingly applied for the purpose of updating 3D models. Shadow detection is the primary step for image interpretation, as shadow causes radiometric distortions. In addition, shadow itself is valuable geometric information. However, shadows are often complicated and environment-dependent. Supervised learning is considered to perform well in detecting shadows when training s les selected from these images are available. Unfortunately, manual labeling of images is expensive. Existing 3D models have been used to reconstruct shadows to provide free, computer-generated training s les, i.e., s les free from intensive manual labeling. However, accurate shadow reconstruction for large 3D models consisting of millions of triangles is either difficult or time-consuming. In addition, due to inaccuracy and incompleteness of the model, and different acquisition time between 3D models and images, mislabeling refers to training s les that are shadows but labeled as non-shadows and vice versa. We propose a ray-tracing approach with an effective KD tree construction to feasibly reconstruct accurate shadows for a large 3D model. An adaptive erosion approach is first provided to remove mislabeling effects near shadow boundaries. Next, a comparative study considering four classification methods, quadratic discriminant analysis (QDA) fusion, support vector machine (SVM), K nearest neighbors (KNN) and Random forest (RF), is performed to select the best classification method with respect to capturing the complicated properties of shadows and robustness to mislabeling. The experiments are performed on Dutch Amersfoort data with around 20% mislabels and the Toronto benchmark by simulating mislabels from inverting shadows to non-shadows. RF is tested to give robust and best results with 95.38% overall accuracy (OA) and a value of 0.9 for kappa coefficient (KC) for Amersfoort and around 96% OA and 0.92 KC for Toronto benchmarks when no more than 50% of shadows are inverted. QDA fusion and KNN are tested to be robust to mislabels but their capability to capture complicated properties of shadows is worse than RF. SVM is tested to have a good capability to separate shadow and non-shadows but is largely affected by mislabeled s les. It is shown that RF with free-training s les from existing 3D models is an automatic, effective, and robust approach for shadow detection from VHR images.
Publisher: Elsevier BV
Date: 12-2016
Publisher: SAGE Publications
Date: 2003
DOI: 10.3141/1855-15
Abstract: To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining in idual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.
Publisher: Copernicus GmbH
Date: 31-05-2017
DOI: 10.5194/ISPRS-ARCHIVES-XLII-1-W1-365-2017
Abstract: Abstract. LiDAR data acquisition is recognized as one of the fastest solutions to provide basis data for large-scale topographical base maps worldwide. Automatic LiDAR processing is believed one possible scheme to accelerate the large-scale topographic base map provision by the Geospatial Information Agency in Indonesia. As a progressive advanced technology, Geographic Information System (GIS) open possibilities to deal with geospatial data automatic processing and analyses. Considering further needs of spatial data sharing and integration, the one stop processing of LiDAR data in a GIS environment is considered a powerful and efficient approach for the base map provision. The quality of the automated topographic base map is assessed and analysed based on its completeness, correctness, quality, and the confusion matrix.
Publisher: Copernicus GmbH
Date: 23-09-2019
DOI: 10.5194/ISPRS-ANNALS-IV-4-W8-11-2019
Abstract: Abstract. This paper presents the research and development of a volumetric 3D visibility analysis application to assess public space in regards to safety and security. While most of the academic literature on space visibility concentrates on developing viewsheds from a specific viewpoint, this work integrates dynamic scenarios and real-time calculation of space visibility. Voxel-based spatial representation is utilised as a base for the 3D visibility analysis. Different space configurations are tested illustrating the robustness and usefulness of the developed application. In order to measure and evaluate different safety zones, an innovative approach is tested according to which the number of times each voxel is observed is recorded and further classified into different zones. In this way, an accurate calculation of the observed space can be performed understanding which part of an area of interest is less or more observed. The research has shown great potential for first responders, researchers, architects and safety experts. The developed application can be used as a simulation tool to assess the safety of different urban environments and identify potentially vulnerable locations.
Publisher: Copernicus GmbH
Date: 12-09-2017
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W7-579-2017
Abstract: Abstract. In VHR(very high resolution) aerial images, shadows indicating height information are valuable for validating or detecting changes on an existing 3D city model. In the paper, we propose a novel and full automatic approach for shadow detection from VHR images. Instead of automatic thresholding, the supervised machine learning approach is expected with better performance on shadow detection, but it requires to obtain training s les manually. The shadow image reconstructed from an existing 3D city model can provide free training s les with large variety. However, as the 3D model is often not accuracy, incomplete and outdated, a small portion of training s les are mislabeled. The erosion morphology is provided to remove boundary pixels which have high mislabeling possibility from the reconstructed image. Moreover, the quadratic discriminant analysis (QDA) which is resistant to the mislabeling is chosen. Further, two feature domains, RGB and ratio of the hue over the intensity, are analyzed to have complementary effects on better detecting different objects. Finally, a decision fusion approach is proposed to combine the results wisely from preliminary classifications from two feature domains. The fuzzy membership is a confidence measurement and determines the way of making decision, in the meanwhile the memberships are weighted by an entropy measurements to indicate their certainties. The experimental results on two cities in the Netherlands demonstrate that the proposed approach outperforms the two separate classifiers and two stacked-vector fusion approaches.
Publisher: MDPI AG
Date: 21-07-2019
DOI: 10.3390/RS11141727
Abstract: Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables the detection of line segments of arbitrary direction, resulting in high-quality building roof polygons. We tested our method on three different datasets of different characteristics: one new dataset in Makassar, Indonesia, and two benchmark datasets in Vaihingen, Germany. To the best of our knowledge, our algorithm is the first Hough method that is highly adaptable since it works for buildings with edges of different lengths and arbitrary relative orientations. The results prove that our method delivers high completeness (between 90.1% and 96.4%) and correctness percentages (all over 96%). The positional accuracy of the building corners is between 0.2–0.57 m RMSE. The quality rate (89.6%) for the Vaihingen-B benchmark outperforms all existing state of the art methods. Other solutions for the challenging Vaihingen-A dataset are not yet available, while we achieve a quality score of 93.2%. Results with arbitrary directions are demonstrated on the complex buildings around the EYE museum in Amsterdam.
Publisher: Copernicus GmbH
Date: 13-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B4-283-2016
Abstract: The paper presents a very straightforward and effective algorithm to convert a space partitioning, made up of polyhedral objects, into a 3D block of voxels, which is fully occupied, i.e. in which every voxel has a value. In addition to walls, floors, etc. there are 'air' voxels, which in turn may be distinguished as indoor and outdoor air. The method is a 3D extension of a 2D polygon-to-raster conversion algorithm. The input of the algorithm is a set of non-overlapping, closed polyhedra, which can be nested or touching. The air volume is not necessarily represented explicitly as a polyhedron (it can be treated as 'background', leading to the 'default' voxel value). The approach consists of two stages, the first being object (boundary) based, the second scan-line based. In addition to planar faces, other primitives, such as ellipsoids, can be accommodated in the first stage without affecting the second.
Publisher: Copernicus GmbH
Date: 23-04-2014
DOI: 10.5194/ISPRSARCHIVES-XL-4-65-2014
Abstract: Abstract. Change detection on the basis of multi-temporal imagery may lead to false alarms when the image has changed, whereas the scene has not. Geometric image differerences in an unchanged scene may be due to relief displacement, caused by diferent camera positions. Radiometric differences may be caused by changes in illumimation and shadow between the images, caused by a different position of the sun. The effects may be predicted, and after that compensated, if a 3d model of the scene is available. The paper presents an integrated approach to prediction of and compensation for relief displacement, shading and shadow.
Publisher: Elsevier BV
Date: 08-2003
Publisher: SPIE
Date: 08-11-2012
DOI: 10.1117/12.975258
Publisher: Copernicus GmbH
Date: 16-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B5-749-2016
Abstract: (Semi)-automatic facade reconstruction from terrestrial LiDAR point clouds is often affected by both quality of point cloud itself and imperfectness of object recognition algorithms. In this paper, we employ regularities, which exist on façades, to mitigate these problems. For ex le, doors, windows and balconies often have orthogonal and parallel boundaries. Many windows are constructed with the same shape. They may be arranged at the same lines and distance intervals, so do different windows. By identifying regularities among objects with relatively poor quality, these can be applied to calibrate the objects and improve their quality. The paper focuses on the regularities among the windows, which is the majority of objects on the wall. Regularities are classified into three categories: within an in idual window, among similar windows and among different windows. Nine cases are specified as a reference for exploration. A hierarchical clustering method is employed to identify and apply regularities in a feature space, where regularities can be identified from clusters. To find the corresponding features in the nine cases of regularities, two phases are distinguished for similar and different windows. In the first phase, ICP (iterative closest points) is used to identify groups of similar windows. The registered points and a number of transformation matrices are used to identify and apply regularities among similar windows. In the second phase, features are extracted from the boundaries of the different windows. When applying regularities by relocating windows, the connections, called chains, established among the similar windows in the first phase are preserved. To test the performance of the algorithms, two datasets from terrestrial LiDAR point clouds are used. Both show good effects on the reconstructed model, while still matching with original point cloud, preventing over or under-regularization.
Publisher: Elsevier BV
Date: 05-1998
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-1998
DOI: 10.1109/36.673673
Publisher: Copernicus GmbH
Date: 19-09-2018
DOI: 10.5194/ISPRS-ANNALS-IV-4-67-2018
Abstract: Abstract. Representation of scenes on the Earth surface by using voxels is gaining attention because of its suitability for integrating heterogeneous data sources in simulations and quantitative models. Computation of shadows in such models is needed, for ex le, to obtain crop suitability of agricultural fields in the presence of trees and buildings, or to analyze urban heat island causes and effects. We present an efficient algorithm to compute which of the voxels in a dataset receive direct sunlight, given the solar azimuth and elevation angles. The algorithm can work with multiple (sparse and dense) voxel storage strategies.
Publisher: Copernicus GmbH
Date: 12-09-2017
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W7-557-2017
Abstract: Abstract. The integration of computer vision and photogrammetry to generate three-dimensional (3D) information from images has contributed to a wider use of point clouds, for mapping purposes. Large-scale topographic map production requires 3D data with high precision and accuracy to represent the real conditions of the earth surface. Apart from LiDAR point clouds, the image-based matching is also believed to have the ability to generate reliable and detailed point clouds from multiple-view images. In order to examine and analyze possible fusion of LiDAR and image-based matching for large-scale detailed mapping purposes, point clouds are generated by Semi Global Matching (SGM) and by Structure from Motion (SfM). In order to conduct comprehensive and fair comparison, this study uses aerial photos and LiDAR data that were acquired at the same time. Qualitative and quantitative assessments have been applied to evaluate LiDAR and image-matching point clouds data in terms of visualization, geometric accuracy, and classification result. The comparison results conclude that LiDAR is the best data for large-scale mapping.
Publisher: Springer Berlin Heidelberg
Date: 1999
Publisher: Elsevier BV
Date: 12-2006
Location: Netherlands
No related grants have been discovered for Bernardus Gorte.