Ben Gorte

A consortium called Mutatis Mutandis (MutMut), consisting of three Universities and eight producers and users of geo-information, was established in the Netherlands to streamline change detection on a national level. After preliminary investigations concerning market feasibility, three actions are being taken by MutMut: (1) construction of a centralised, web based repository for collection and exchange of changes detected by various actors, (2) design and implementation of an image based, automated change detection methodology, and (3) widening the scope of the project in a European context. This paper focusses on the second action. Objects in the database are used to formulate hypotheses, for which newer imagery may or may not provide evidence. In the latter case, older imagery is considered as well. If evidence for the hypothesis could still be found there, a possible change is detected. The paper shows preliminary results of a first implementation of this methodology. 1.

The influences of low vegetation on airborne laser scanning are studied. High vegetation is removed by filtering, but low vegetation causes systematic errors in digital terrain models. Many researchers have reported that the measurements are too high. The investigation of influences on the laser range measurement improves the understanding of the technology in use and gives explanations for the observed errors. The possibilities of correcting the data with information on the vegetation type are studied, using ground truth data from terrestrial measurements as reference. An alternative approach using texture measures, which does not require information on the land cover type, is presented. Texture has previously been defined for digital images and its equivalent for point clouds is presented here. 1

The influence of low vegetation on laser scanning and its disturbing effect (a systematic positive height shift) during DTM generation is well recognized. Based on our experience in a previous study of estimating the effect of medium-height vegetation (shrubs and bushes) in rough terrain by using point-cloud based co-occurrence texture methods, we now investigate the effect of lower vegetation, such as grass, in very flat marshland conditions. When vegetation is very low, and thus the effect very small, it appears difficult to separate the systematic shift from random measurement noise. When the shift becomes larger, however, it shows significant correlation with texture measures such as slope texture and standard deviation. These measures are derived from the laser data itself and do not require any additional information.

There has been a steady increase in applications that rely on crowdsensing to gather data for analysis purposes. Crowdsensing enables the use of dynamic sensors to collect data on static objects of interest. However, using dynamic sensors in this way causes a problem. The focus of the collected data is on the position of the sensor, not on the object of interest. This results in difficulties in tracking the object of interest in terms of what part of the data from the dynamic sensor describes the object of interest. To shift the focus from the dynamic sensors to a static object, the virtual sensor is introduced. A virtual sensor enables the grouping of data from different dynamic sensors into a single virtual sensor based on measurement positions. The data from the multiple dynamic sensors can be analyzed to provide information per virtual sensor. The data structure of a visual sensor is close to the SensorThings API data structure, which can be expanded to support virtual sensors b...

Measuring positions, velocities and accelerations/decelerations of individual vehicles in congested traffic with standard traffic monitoring equipment, such as inductive loops, are not feasible. The behavior of drivers in the different traffic situations, as re-quired for microscopic traffic flow models, is still not sufficiently known. Remote sensing and computer vision technology are recently being used to solve this problem. In this study we use video images taken from a helicopter above a fixed point of the highway. We address the problem of tracking the movement of previously detected vehicles through a stabilized video sequence. We combine two approaches, optical flow and matching based tracking, improve them by adding constraints and using scale space. Feature elements, i.e. the corners, lines, regions and outlines of each car, are extracted first. Then, optical-flow is used to find for each pixel in the interior of a car the corresponding pixel in the next image, by insertin...

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 t...

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 th...

Abstract The satellite observed Normalized Difference Vegetation Index (NDVI) time series, which describe the temporal and spatial variability of global terrestrial vegetation, are inevitably contaminated by clouds, aerosol, snow and ice cover. In general all these conditions yield negative deviations in the time series of NDVI. Many time series reconstruction models have been developed to eliminate effect of the negative deviations and most of them perform differently in different applications and regions. The Harmonic Analysis (HA), Double logistic (DL), Asymmetric Gaussian (AG), Whittaker smoother (WS) and Savitzky–Golay filter (SG) are five of the most widely used time series reconstruction models owing to their simplicity of implementation or the capability to extract phenological metrics from the time series. The performance of these models varies with the NDVI signal and the noise distribution, however, and until now there is no consensus on which method outperforms all others under all situations. Since the NDVI signal and the noise distribution are highly dependent on regional climate and land cover, the reconstruction performance is expected to be spatially variable. Thus this study compared the five reconstruction models at pixel scale to provide practical and biome – specific recommendations for future time series reconstruction applications. Specifically, the 14 years raw daily reflectance data and ancillary Quality Assessment (QA) information from the MODIS sensor were used to generate pixel reference series and noisy series. Then the five candidate models were applied to both reference series and noisy series and three reconstruction performance metrics i.e. Overall Reconstruction Error (ORE), Fitting Related Error (FRE), and Normalized Noise Related Error (NNRE), were calculated. Finally, the performance of the five candidate reconstruction models was evaluated by applying the three metrics. The preliminary results showed that when considering ORE only, the Asymmetric Gaussian model outperforms other models over most areas of high latitude boreal region, while the Savitzky–Golay model gives the best reconstruction performance in tropical and subtropical regions. The FRE and the NNRE helped to reveal the main error sources in the reconstruction in different regions. The comparison method developed and applied in this study led to suggest adaptive selection of the best reconstruction model for specific NDVI signals and noise distribution.

With the rapid improvement of LIDAR systems regarding point density and accuracy in relation to the (application dependent) requirements, robustness, efficiency and automation of the modeling process are becoming more important than achieving the highest possible accuracy and modeling detail from the available LIDAR data. Therefore we opt for development of a 2D grid based LIDAR data analysis approach. An important step is detection and parameterization of planar surfaces (roof elements). The paper reviews four methods, based on analysis of gradients, principal components, least squares and hough transforms, respectively. It introduces a series of improvements to the standard usage of each of those methods and shows results from synthetic and real data. 1.

ABSTRACT The paper introduces storage and processing of 3-dimensional point clouds, obtained by terrestrial laser scanning, in the 3D raster domain. The objects under consideration are trees in production orchards. The purpose is to automatically identify the structure of such trees in terms of the number of branches, their lengths and their thicknesses. An important step in the process is skeletonization. On the basis of a previously developed methodology, a new skeletonization algorithm is developed, which delivers improved results.

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 samples 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 samples, i.e., samples free from intensive manual labeling. However, accurate shadow reconstruction for large-scene 3D models consisting of millions of triangles is either difficult or time-consuming. In addition, mislabeled samples affect classi...

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.

ABSTRACT: Glaciers are interesting phenomena to scientists, mountaineers and tourists. Glaciers have a great impact on the local economy, power generation and water supply. Furthermore, thebehaviour of glaciers is influenced by climate variations, such as changes in temperature. Monitoring glaciers can therefore givevaluable,insight to glaciologists. Two aspects of glaciers that can be monitored are the delineation of a glacier and

The investigation of single trees in a forest is of ecological and economical interest. One aim is to capture the geometric aspects of a tree: the length and diameter of the trunk and individual branches, the change of the radius along the branch and similar measures. These measures can be determined automatically from terrestrial laser scanner data. The conditions for scanning in the forest, but also the irregular structure and surface of trees aggravate the reconstruction process. The branches of the trees are locally modelled by circular cylinders. With the radius, the axis direction and the axis position the main parameters of interest are captured. We describe a set of algorithms for automatically tting and tracking cylinders along branches and reconstructing the entire tree. Especially for coniferous trees the computation of an outer hull, giving the extent in different directions and at different heights is an alternative, as the dense foliage coverage renders a distinction b...

Point clouds obtained by aerial laser scanning are a convenient input source for high resolution 2.5d elevation models, such as the Dutch AHN-2. More challenging is the fully automatic reconstruction of 3d city models. An actual demand for a combined 2.5d terrain and d city model for an urban hydrology application led to the design of an extension to the well-known Delaunay triangulated irregular networks (TINs) as to accommodate vertical walls. In addition we introduce methods to generate and refine models adhering to our data structure. These are based on combining two approaches: a representation of the TIN using stars of vertices and triangles, together with segmenting the TIN on the basis of coplanarity of adjacent triangles. The approach is supposed to deliver the complete model including walls at the correct locations, without relying on additional map data, as these often lack completeness, actuality and accuracy, and moreover most of the time do not account for parts facade...

... Jaap Besemer Roland Billen Lars Bodum, Antonio Camara Jun Chen Volker Coors Oscar Custers Naser El-Sheimy Andrea Fabbri Elfriede M ... Zlatanova Pieter Jonker Allison Kealy Tjeu Lemmens Rainer Malaka Mark Millman Chris Parker Friso Penninga Wilko Quak Alias Abdul ...

ABSTRACT Morphodynamic changes at sandy coasts, as are dominant in the Netherlands, are typically monitored at yearly intervals by means of airborne laser scanning. Meanwhile it is recognised that beach morphodynamics is not a regular process and is strongly correlated to meteorological conditions. A series of 25 terrestrial laser scans of a beach experiment is analysed, obtained during a time period of 88 hours, characterised by changing weather conditions including strong wind and rain. It is shown that after a conversion to a cylindrical grid, different deformation regimes can be identified and rates of elevation changes below 1 mm per hour can be detected. Simultaneously, an analysis of distances between targets through time demonstrates the sensitivity of the used measurement set-up to the harsh weather conditions. Les transformations morphodynamiques de côtes sableuses, qui constituent un phénomène d’importance majeure aux Pays-Bas, font l’objet d’une surveillance typiquement annuelle par balayage laser aéroporté. On sait toutefois que la morphodynamique des plages est un processus irrégulier qui est fortement lié aux conditions météorologiques. Une série de 25 acquisitions par balayage laser terrestre est analysée. Ces données ont été obtenues sur une période de 88 heures caractérisée par des conditions météorologiques changeantes avec un vent fort et de la pluie. Il est démontré qu’après conversion en une grille cylindrique, différents régimes de déformation peuvent être identifiés et que des variations altimétriques inférieures au millimètre par heure peuvent être détectées. Par ailleurs, une analyse des distances entre cibles au cours du temps montre la sensibilité des mesures mises en œuvre aux conditions météorologiques extrêmes. Morphologische Veränderungen an Sandküsten, wie sie in in den Niederlanden vorherrschen, werden üblicherweise in jährlichen Abständen durch flugzeuggestütztes Laserscanning überwacht. Mittlerweile ist festgestellt, dass die morphologische Dynamik eines Strandes kein regelmäßiger Prozess ist, sondern stark mit den meteorologischen Bedingungen korreliert. Eine Folge von 25 Scans mit Terrestrischem Laserscanning wurde in 88 Stunden an einem Strandabschnitt aufgenommen und analysiert. Die Wetterbedingungen waren sehr wechselhaft, inklusive starker Winde und Regen. Durch die Umwandlung auf ein zylindrisches Gitter, können verschiedene Deformationszustände identifiziert und Höhenänderungen unter einem Millimeter pro Stunde detektiert werden. Gleichzeitig konnte durch eine Analyse der Distanzen zwischen den Zielpunkten über die Zeit die Empfindlichkeit der Messanordnung für die rauhen Wetterbedingungen gezeigt werden. Los cambios morfodinámicos en playas, costa dominante en Holanda, se monitorizan usualmente con campañas aéreas anuales de escaneado láser. Por otro lado los cambios morfodinámicos de las playas lejos de ser un proceso estacional están fuertemente correlacionados con las condiciones meteorológicas. Se analiza una serie de 25 capturas de láser terrestre obtenidas durante un periodo de 88 horas, el cual se caracteriza por condiciones meteorológicas variables incluyendo fuertes vientos y lluvia. Se demuestra que pueden identificarse distintos regímenes de deformación y que pueden detectarse ratios de cambio de elevación por debajo de un milímetro a la hora. Simultáneamente un análisis de las distancias a lo largo del tiempo entre objetos identificados demuestra la sensibilidad del equipo usado a las malas condiciones meteorológicas.

The paper describes an iterative extension to maximum a posteriori (MAP) supervised classification methods. A posteriori probabilities per class are used for classification as well as to obtain class area estimates. From these, an updated set of prior probabilities is calculated and used in the next iteration. The process converges to statistically correct area estimates. The iterative process can be combined effectively with a stratification of the image, which is made on the basis of additional map data. Moreover, it relies on the sample sets being representative. Therefore, the method is shown to be well applicable in combination with an existing GIS. The paper gives a description of the procedure and provides a mathematical foundation. An example is presented to distinguish residential, industrial, and greenhouse classes. A significant improvement of the classification was obtained