Unmanned aerial systems (UASs) have become of considerable private and commercial interest for a variety of jobs and entertainment in the past 10 years. This paper is a literature review of the state of practice for the United States bridge inspection programs and outlines how automated and unmanned bridge inspections can be made suitable for present and future needs. At its best, current technology limits UAS use to an assistive tool for the inspector to perform a bridge inspection faster, safer, and without traffic closure. The major challenges for UASs are satisfying restrictive Federal Aviation Administration regulations, control issues in a GPS-denied environment, pilot expenses and availability, time and cost allocated to tuning, maintenance, post-processing time, and acceptance of the collected data by bridge owners. Using UASs with self-navigation abilities and improving image-processing algorithms to provide results near real-time could revolutionize the bridge inspection industry by providing accurate, multi-use, autonomous three-dimensional models and damage identification.

/pdf/bridge-inspection-human-performance-unmanned-aerial-systems-41wnrmmga3.pdf

Bridge inspection: human performance, unmanned aerial systems and automation

Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in Austin, Texas, with land parcel boundaries delineated by the Travis Central Appraisal District of Travis County, Texas, is tested for the approach. We derive fifty parcel attributes from relevant geographic information system (GIS) and remote sensing data and use them to discriminate among nine urban land uses: single family, multifamily, commercial, office, industrial, civic, open space, transportation, and undeveloped. Half of the 33,025 parcels in the study area are used as training data for land use classification and the other half are used as testing data for accuracy assessment. The best result with a decision tree classification algorithm has an overall accuracy of 96 percent and a kappa coefficient of...

/pdf/using-geometrical-textural-and-contextual-information-of-395hfj1ja2.pdf

Using Geometrical, Textural, and Contextual Information of Land Parcels for Classification of Detailed Urban Land Use

Unmanned Aerial Vehicles (UAVs) offer several new possibilities in a wide range of applications. One example is the 3D reconstruction of buildings. In former times this was either restricted by earthbound vehicles to the reconstruction of facades or by air-borne sensors to generate only very coarse building models. This paper describes an approach for fully automatic image-based 3D reconstruction of buildings using UAVs. UAVs are able to observe the whole 3D scene and to capture images of the object of interest from completely different perspectives. The platform used by this work is a Falcon 8 octocopter from Ascending Technologies. A slightly modiﬁed high-resolution consumer camera serves as sensor for data acquisition. The ﬁnal 3D reconstruction is computed ofﬂine after image acquisition and follows a reconstruction process originally developed for image sequences obtained by earthbound vehicles. The per- formance of the described method is evaluated on benchmark datasets showing that the achieved accuracy is high and even comparable with Light Detection and Ranging (LIDAR). Additionally, the results of the application of the complete processing-chain starting at image acquisition and ending in a dense surface-mesh are presented and discussed.

/pdf/three-dimensional-building-reconstruction-using-images-4ba9l1ysvz.pdf

Three-Dimensional Building Reconstruction Using Images Obtained by Unmanned Aerial Vehicles

We present a framework to segment cultural and natural features, given 3D aerial scans of a large urban area, and (optionally) registered ground level scans of the same area. This system provides a primary step to achieve the ultimate goal of detecting every object from a large number of varied categories, from antenna to power plants. Our framework first identifies local patches of the ground surface and roofs of buildings. This is accomplished by tensor voting that infers surface orientation from neighboring regions as well as local 3D points. We then group adjacent planar surfaces with consistent pose to find surface segments and classify them as either the terrain or roofs of buildings. The same approach is also applied to delineate vertical faces of buildings, as well as free-standing vertical structures such as fences. The inferred large structures are then used as geometric context to segment linear structures, such as power lines, and structures attached to walls and roofs from remaining unclassified 3D points in the scene. We demonstrate our system on real LIDAR datasets acquired from typical urban regions with areas of a few square kilometers each, and provide a quantitative analysis of performance using externally provided ground truth.

Urban scene understanding from aerial and ground LIDAR data

In this paper, a multi-resolution hybrid approach is proposed for the reconstruction of building models from point clouds of lidar data. The detection of the main roof planes is obtained through a polyhedral approach, whereas the models of appended parts, in this case the dormers, are reconstructed by adopting a model-driven approach. Clustering of the roof points in a multi-resolution space is based on the fuzzy c-mean in the polyhedral section of this hybrid approach. A weighted plane algorithm is developed in order to determine the planes of each cluster. The verification of planes between multi-resolution spaces adopts a method based on a least squares support vector machine that, in the model-driven section, is applied for detecting types of projecting structures. A method is then developed to determine the dormer models’ parameters. Finally, the detection of boundary roof lines is obtained through a customised fuzzy Hough transform. The paper outlines the concept of the algorithms and the processing chain, and illustrates the results obtained by applying the technique to buildings of different complexities.

Resume

Cet article propose une approche hybride multi-resolution pour la reconstruction de modeles de bâtiments a partir de nuages de points lidar. La detection des principaux plans des toits est obtenue par une approche polyedrique, tandis que les modeles de structures annexes, en l’occurrence les mansardes, sont reconstruits en adoptant une approche parametrique. La segmentation des points du toit dans un espace multi-resolution est basee sur la c-moyenne floue dans la composante polyedrique de cette approche hybride. Un algorithme est developpe pour determiner, avec une ponderation, le plan correspondant a chaque segment. La validation des plans obtenus entre des espaces multi-resolution adopte une methode basee sur les moindres carres des machines a vecteur de support, methode qui, dans la composante parametrique, sert a detecter les differents types de structures annexes. Une methode est ensuite developpee pour determiner les parametres des modeles de mansardes. Enfin, la detection des lignes de bordure de toits s’obtient par l’adaptation d’une transformee de Hough floue. L’article presente le principe des algorithmes et la chaine de traitement, et illustre les resultats obtenus en appliquant la technique a des bâtiments de complexites variees.

Zusammenfassung

Dieser Beitrag stellt einen hybriden Ansatz unter Nutzung von Mehrfachauflosungen zur Rekonstruktion von Gebaudemodellen aus Lidar Punktwolken vor. Die Erkennung der Hauptebenen eines Daches erfolgt durch einen polyedrischen Ansatz. Dier Modellierung von nachgeordneten Strukturen, hier Dachausbauten, erfolgt durch einen modellbasierten Ansatz. Der Fuzzy C-Mean Algorithmus wird genutzt, um ein Clustering der Dachpunkte fur den polyedrischen Teil des hybriden Ansatzes durchzufuhren. Ein gewichteter Ebenen-Algorithmus wurde entwickelt, um die Ebenen eines jeden Clusters zu bestimmen. Die Verifizierung der Ebenen in verschiedenen Auflosungsraumen adaptiert eine Methode, die sich einer Kleinsten Quadrate Stutzvektormaschine bedient, und wird im modellgetriebenen Teil des Ansatzes zur Erkennung der Typen der untergeordneten Strukturen verwendet. Eine weitere entwickelte Methode bestimmt die Parameter der Dachausbauten. Die abschliesende Erkennung der Dachrandlinien erfolgt durch eine angepasste Fuzzy Hough Transformation. Der Beitrag gibt einen Uberblick uber das Konzept des Algorithmus und die Prozesskette und stellt beispielhaft Ergebnisse der Technik an Gebauden unterschiedlicher Komplexitat vor.

Resumen

En el articulo se propone una aproximacion hibrida de multi-resolucion para la reconstruccion de modelos de edificios partiendo de nubes de puntos lidar. La deteccion de los planos principales de tejado se obtiene mediante una aproximacion poliedrica, mientras que los modelos de estructuras anexas, en este caso buhardillas, se reconstruyen adoptando una aproximacion dirigida por el modelo. La agrupacion de puntos de tejado en un espacio de multi-resolucion se basa en la media-c difusa en la seccion poliedrica de esta aproximacion hibrida. Se ha desarrollado un algoritmo de planos ponderados para determinar los planos de cada agrupacion de puntos. La verificacion de los planos entre espacios de multi-resolucion adopta un metodo basado en maquinas de vectores de soporte de minimos cuadrados. En el caso de la seccion dirigida por el modelo, se aplica para detectar los tipos de estructuras anexas. Entonces se determinan los parametros de modelo para la buhardilla. Finalmente, la deteccion de las lineas de frontera de los tejados se obtiene mediante una transformacion de Hough difusa adaptada. En el articulo se resume el concepto de los algoritmos y la cadena de proceso e ilustra los resultados obtenidos por aplicar la tecnica a edificios de complejidades diferentes.

A Multi-Resolution Hybrid Approach for Building Model Reconstruction from Lidar Data

Airborne LiDAR data can be used to create 3D terrain models useful in augmented or virtual reality in which real-world landscapes are incorporated. Automatic generation of 3D models of man-made structures such as buildings is essential for creating these detailed models. We present a method that builds a 3D mesh, segments the triangles, and forms planar facets that correspond to a roof structure of a target building. We illustrate our method with real-world data sets.

/pdf/3d-building-reconstruction-from-lidar-data-2dxxp79i17.pdf

3D building reconstruction from LiDAR data

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3D building reconstruction from LiDAR data