On September 14, 2015 at 09:50:45 UTC the two detectors of the Laser Interferometer Gravitational-Wave Observatory simultaneously observed a transient gravitational-wave signal. The signal sweeps upwards in frequency from 35 to 250 Hz with a peak gravitational-wave strain of 1.0×10(-21). It matches the waveform predicted by general relativity for the inspiral and merger of a pair of black holes and the ringdown of the resulting single black hole. The signal was observed with a matched-filter signal-to-noise ratio of 24 and a false alarm rate estimated to be less than 1 event per 203,000 years, equivalent to a significance greater than 5.1σ. The source lies at a luminosity distance of 410(-180)(+160)  Mpc corresponding to a redshift z=0.09(-0.04)(+0.03). In the source frame, the initial black hole masses are 36(-4)(+5)M⊙ and 29(-4)(+4)M⊙, and the final black hole mass is 62(-4)(+4)M⊙, with 3.0(-0.5)(+0.5)M⊙c(2) radiated in gravitational waves. All uncertainties define 90% credible intervals. These observations demonstrate the existence of binary stellar-mass black hole systems. This is the first direct detection of gravitational waves and the first observation of a binary black hole merger.

/pdf/the-observation-of-gravitational-waves-from-a-binary-black-58srpupgg9.pdf

The Observation of Gravitational Waves from a Binary Black Hole Merger

Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of
LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for
DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage
and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity
characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented.

/pdf/airborne-lidar-for-dem-generation-some-critical-issues-sxb3b6yug9.pdf

Airborne LiDAR for DEM generation: some critical issues

Rapid damage assessment after natural disasters (eg, earthquakes) and violent conflicts (eg, war-related destruction) is crucial for initiating effective emergency response actions Remote-sensing satellites equipped with very high spatial resolution (VHR) multispectral and synthetic aperture radar (SAR) imaging sensors can provide vital information due to their ability to map the affected areas with high geometric precision and in an uncensored manner In this paper, we present a novel method that detects buildings destroyed in an earthquake using pre-event VHR optical and post-event detected VHR SAR imagery The method operates at the level of individual buildings and assumes that they have a rectangular footprint and are isolated First, the 3-D parameters of a building are estimated from the pre-event optical imagery Second, the building information and the acquisition parameters of the VHR SAR scene are used to predict the expected signature of the building in the post-event SAR scene assuming that it is not affected by the event Third, the similarity between the predicted image and the actual SAR image is analyzed If the similarity is high, the building is likely to be still intact, whereas a low similarity indicates that the building is destroyed A similarity threshold is used to classify the buildings We demonstrate the feasibility and the effectiveness of the method for a subset of the town of Yingxiu, China, which was heavily damaged in the Sichuan earthquake of May 12, 2008 For the experiment, we use QuickBird and WorldView-1 optical imagery, and TerraSAR-X and COSMO-SkyMed SAR data

/pdf/earthquake-damage-assessment-of-buildings-using-vhr-optical-suewo0xzvz.pdf

Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery

This paper presents a solution framework for the segmentation and reconstruction of polyhedral building roofs from aerial LIght Detection And Ranging (lidar) point clouds. The eigenanalysis is first carried out for each roof point of a building within its Voronoi neighborhood. Such analysis not only yields the surface normal for each lidar point but also separates the lidar points into planar and nonplanar ones. In the second step, the surface normals of all planar points are clustered with the fuzzy k-means method. To optimize this clustering process, a potential-based approach is used to estimate the number of clusters, while considering both geometry and topology for the cluster similarity. The final step of segmentation separates the parallel and coplanar segments based on their distances and connectivity, respectively. Building reconstruction starts with forming an adjacency matrix that represents the connectivity of the segmented planar segments. A roof interior vertex is determined by intersecting all planar segments that meet at one point, whereas constraints in the form of vertical walls or boundary are applied to determine the vertices on the building outline. Finally, an extended boundary regularization approach is developed based on multiple parallel and perpendicular line pairs to achieve topologically consistent and geometrically correct building models. This paper describes the detail principles and implementation steps for the aforementioned solution framework. Results of a number of buildings with diverse roof complexities are presented and evaluated.

Segmentation and Reconstruction of Polyhedral Building Roofs From Aerial Lidar Point Clouds

LIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents a three-stage framework for a robust automatic classification of raw LIDAR data as buildings, ground and vegetation, followed by a reconstruction of 3D models of the buildings. In the first stage the raw data are filtered and interpolated over a grid. In the second stage, first a double raw data segmentation is performed and then geometric and topological relationships among regions resulting from segmentation are computed and stored in a knowledge base. In the third stage, a rule-based scheme is applied for the classification of the regions. Finally, polyhedral building models are reconstructed by analysing the topology of building outlines, building roof slopes and eaves lines. Results obtained on data sets with different ground point density, gathered over the town of Pavia (Italy) with Toposys and Optech airborne laser scanning systems, are shown to illustrate the effectiveness of the proposed approach.

Complete classification of raw LIDAR data and 3D reconstruction of buildings

Automated approaches to building detection in multi-source aerial data are important in many applications, including map updating, city modeling, urban growth analysis and monitoring of informal settlements. This paper presents a comparative analysis of different methods for automated building detection in aerial images and laser data at different spatial resolutions. Five methods are tested in two study areas using features extracted at both pixel level and object level, but with the strong prerequisite of using the same training set for all methods. The evaluation of the methods is based on error measures obtained by superimposing the results on a manually generated reference map of each area. The results in both study areas show a better performance of the Dempster-Shafer and the AdaBoost methods, although these two methods also yield a number of unclassified pixels. The method of thresholding a normalized DSM performs well in terms of the detection rate and reliability in the less vegetated Mannheim study area, but also yields a high rate of false positive errors. The Bayesian methods perform better in the Memmingen study area where buildings have more or less the same heights.

https://repository.tudelft.nl/islandora/object/uuid%3A5d5889cf-a4de-4594-bc8b-188416654701/datastream/OBJ/download

Performance evaluation of automated approaches to building detection in multi-source aerial data

Panoramic images have increasingly interesting applications and they are extensively used for cultural heritage documentation and virtual 3D environments. Moreover, it has been proven that they can contain good metric content. This paper describes a state-of-the-art photogrammetric tool based on spherical panoramas. The fundamental photogrammetric equations are reviewed and two real applications are examined in order to illustrate its performance in terms of accuracy and efficiency. As a first step towards the automation of the orientation and restitution process, the epipolar geometry of spherical panoramic images has also been investigated. With a view to its integration in photogrammetric tools, the shape of the epipolar line is studied both in 3D space and as a 2D mapping; the results obtained with simulation data are analysed.

Resume

Les images panoramiques ont des applications de plus en plus interessantes et sont tres souvent utilisees pour la documentation du patrimoine culturel et pour creer des environnements virtuels 3D. En outre, il est prouve qu'elles peuvent avoir un bon contenu metrique. Cet article decrit un nouvel outil photogrammetrique base sur des panoramas spheriques. Apres un rappel des equations fondamentales de la photogrammetrie, les performances de cet outil sont illustrees par deux applications concretes, en termes de precision et d'efficacite. Dans un but d'automatisation des processus d'orientation et de restitution, la geometrie epipolaire des images panoramiques spheriques est etudiee. En vue de prendre en compte la ligne epipolaire dans des outils photogrammetriques, sa forme est etudiee tant dans l'espace 3D que dans sa cartographie en 2D; les resultats obtenus sur des donnees simulees sont analyses.

Zusammenfassung

Panoramabilder finden heutzutage mehr und mehr interessante Anwendungen und werden derzeit auch in umfassender Weise fur die Dokumentation des Kulturerbes und virtueller 3D-Umgebungen eingesetzt. Daruber hinaus hat es sich erwiesen, dass sie einen guten metrischen Inhalt haben. In diesem Artikel wird nach der Prasentation der fur spharische Panoramabilder grundlegenden photogrammetrischen Gleichungen ein neues photogrammetrisches Instrument vorgestellt. Die Epipolargeometrie von spharischen Panoramabildern wird in Hinblick auf die Automatisierung der Orientierungs- und Wiedergabeprozesse untersucht. Insbesondere wird die Form der Epipolarlinien sowohl im 3D-Raum als auch in seiner 2D-Darstellung untersucht. Weiterhin werden die mittels simulierter Daten erzielten Ergebnisse in Hinblick auf eine Software-Implementierung in der Photogrammetrie analysiert.

Resumen

Las imagenes panoramicas tienen aplicaciones cada vez mas interesantes y son usadas extensivamente en la documentacion del patrimonio cultural y en entornos virtuales en 3D. Ademas, se ha demostrado que pueden tener un buen contenido metrico. En este articulo se describe el estado del arte de las herramientas fotogrametricas basadas en panoramas esfericos, se revisan las ecuaciones fundamentales de la fotogrametria y se examinan dos aplicaciones reales para ilustrar su rendimiento en terminos de precision y de eficiencia. Como primer paso hacia la automatizacion de los procesos de orientacion y restitucion, se ha investigado la geometria epipolar de las imagenes panoramicas esfericas. En particular, se ha estudiado la forma de la linea epipolar en el espacio 3D y en su proyeccion 2D, con la finalidad de su implementacion en herramientas fotogrametricas; analizandose los resultados obtenidos con datos simulados.

Photogrammetric Processing of Spherical Panoramas

Unmanned aerial vehicle-based LiDAR survey provides very-high-density point clouds, which involve very rich information about forest detailed structure, allowing for detection of individual trees, ...

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2018.1474722

Single-tree detection in high-density LiDAR data from UAV-based survey

A strategy for building extraction and building reconstruction from dense LIDAR data is presented. Roofs are modeled as plane surfaces, connected along ridges and bordered by the eaves lines. Initial segmentation of terrain and non-terrain features is performed on grid data; after segmentation by region growing and region topology, classification is achieved based on a hierarchical set of aggregation rules. Areas labelled as potential buildings are further segmented in plane surfaces (roofs slopes) based on gradient orientation and plane fitting by RANSAC; a similar procedure applies to edge pixels to extract eaves lines. The topology of the roof slopes and walls is reconstructed, deriving also roof ridges and roof corners. A 3D model of the building is thereafter obtained which can be imported in CAD and visualization environments, such as those provided by the VTP group. Results from a laser scanning survey with a ground resolution of 1 m are presented for a suburban area in Pavia.

/pdf/building-reconstruction-and-visualization-from-lidar-data-2tyskfo2u9.pdf

Carla Nardinocchi

Papers

Complete classification of raw LIDAR data and 3D reconstruction of buildings

Performance evaluation of automated approaches to building detection in multi-source aerial data

Photogrammetric Processing of Spherical Panoramas

Single-tree detection in high-density LiDAR data from UAV-based survey

Building Reconstruction and Visualization from LIDAR Data