Investigation on roof segmentation for 3D building reconstruction from aerial LIDAR point clouds

Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimation, estimation of the propagation of noise in an urban environment, routing and accessibility, flood or seismic damage assessment). With advancements in airborne laser scanning (ALS), 3D modeling of urban topography has increased its potential to automatize extraction of the characteristics of individual buildings. In 3D building modeling from light detection and ranging (LIDAR) point clouds, one major challenging issue is how to efficiently and accurately segment building regions and extract rooftop features. This study aims to present an investigation and critical comparison of two different fully automatic roof segmentation approaches for 3D building reconstruction. In particular, the paper presents and compares a cluster-based roof segmentation approach that uses (a) a fuzzy c-means clustering method refined through a density clustering and connectivity analysis, and (b) a region growing segmentation approach combined with random sample consensus (RANSAC) method. In addition, a robust 2.5D dual contouring method is utilized to deliver watertight 3D building modeling from the results of each proposed segmentation approach. The benchmark LIDAR point clouds and related reference data (generated by stereo plotting) of 58 buildings over downtown Toronto (Canada), made available to the scientific community by the International Society for Photogrammetry and Remote Sensing (ISPRS), have been used to evaluate the quality of the two proposed segmentation approaches by analysing the geometrical accuracy of the roof polygons. Moreover, the results of both approaches have been evaluated under different operating conditions against the real measurements (based on archive documentation and celerimetric surveys realized by a total station system) of a complex building located in the historical center of Matera (UNESCO world heritage site in southern Italy) that has been manually reconstructed in 3D via traditional Building Information Modeling (BIM) technique. The results demonstrate that both methods reach good performance metrics in terms of geometry accuracy. However, approach (b), based on region growing segmentation, exhibited slightly better performance but required greater computational time than the clustering-based approach

Extraction of buildings from high-resolution satellite data and airborne Lidar

Automatic building extraction is a difficult object recognition problem due to a high complexity of the scene content and the object representation. There is a dilemma to select appropriate building models to be reconstructed; the models have to be generic in order to represent a variety of building shape, whereas they also have to be specific to differentiate buildings from other objects in the scene. Therefore, a scientific challenge of building extraction lies in constructing a framework for modelling building objects with appropriate balance between generic and specific models. This thesis investigates a synergy of IKONOS satellite imagery and airborne LIDAR data, which have recently emerged as powerful remote sensing tools, and aims to develop an automatic system, which delineates building outlines with more complex shape, but by less use of geometric constraints. The method described in this thesis is a two step procedure: building detection and building description. A method of automatic building detection that can separate individual buildings from surrounding features is presented. The process is realized in a hierarchical strategy, where terrain, trees, and building objects are sequentially detected. Major research efforts are made on the development of a LIDAR filtering technique, which automatically detects terrain surfaces from a cloud of 3D laser points. The thesis also proposes a method of building description to automatically reconstruct building boundaries. A building object is generally represented as a mosaic of convex polygons. The first stage is to generate polygonal cues by a recursive intersection of both datadriven and model-driven linear features extracted from IKONOS imagery and LIDAR data. The second stage is to collect relevant polygons comprising the building object and to merge them for reconstructing the building outlines. The developed LIDAR filter was tested in a range of different landforms, and showed good results to meet most of the requirements of DTM generation and building detection. Also, the implemented building extraction system was able to successfully reconstruct the building outlines, and the accuracy of the building extraction is good enough for mapping purposes

Extraction of buildings from high-resolution satellite data and airborne LIDAR

Automatic building extraction is a difficult object recognition problem due to a high complexity of the scene content and the object representation. There is a dilemma to select appropriate building models to be reconstructed; the models have to be generic in order to represent a variety of building shape, whereas they also have to be specific to differentiate buildings from other objects in the scene. Therefore, a scientific challenge of building extraction lies in constructing a framework for modelling building objects with appropriate balance between generic and specific models. This thesis investigates a synergy of IKONOS satellite imagery and airborne LIDAR data, which have recently emerged as powerful remote sensing tools, and aims to develop an automatic system, which delineates building outlines with more complex shape, but by less use of geometric constraints. The method described in this thesis is a two step procedure: building detection and building description. A method of automatic building detection that can separate individual buildings from surrounding features is presented. The process is realized in a hierarchical strategy, where terrain, trees, and building objects are sequentially detected. Major research efforts are made on the development of a LIDAR filtering technique, which automatically detects terrain surfaces from a cloud of 3D laser points. The thesis also proposes a method of building description to automatically reconstruct building boundaries. A building object is generally represented as a mosaic of convex polygons. The first stage is to generate polygonal cues by a recursive intersection of both datadriven and model-driven linear features extracted from IKONOS imagery and LIDAR data. The second stage is to collect relevant polygons comprising the building object and to merge them for reconstructing the building outlines. The developed LIDAR filter was tested in a range of different landforms, and showed good results to meet most of the requirements of DTM generation and building detection. Also, the implemented building extraction system was able to successfully reconstruct the building outlines, and the accuracy of the building extraction is good enough for mapping purposes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Detection of fragmented rectangular enclosures in very high resolution remote sensing images

We develop an approach for the detection of ruins of livestock enclosures (LEs) in alpine areas captured by high-resolution remotely sensed images. These structures are usually of approximately rectangular shape and appear in images as faint fragmented contours in complex background. We address this problem by introducing a rectangularity feature that quantifies the degree of alignment of an optimal subset of extracted linear segments with a contour of rectangular shape. The rectangularity feature has high values not only for perfectly regular enclosures but also for ruined ones with distorted angles, fragmented walls, or even a completely missing wall. Furthermore, it has a zero value for spurious structures with less than three sides of a perceivable rectangle. We show how the detection performance can be improved by learning a linear combination of the rectangularity and size features from just a few available representative examples and a large number of negatives. Our approach allowed detection of enclosures in the Silvretta Alps that were previously unknown. A comparative performance analysis is provided. Among other features, our comparison includes the state-of-the-art features that were generated by pretrained deep convolutional neural networks (CNNs). The deep CNN features, although learned from a very different type of images, provided the basic ability to capture the visual concept of the LEs. However, our handcrafted rectangularity-size features showed considerably higher performance.European Prehistor

Using an anisotropic diffusion scale-space for the detection and delineation of shacks in informal settlement imagery

PhD, Faculty of Engineering and the Built Environment, University of the Witwatersrand, 2010Informal settlements are a growing world-wide phenomenon. Up-to-date spatial information mapping settlements is essential for a variety of end-user applications from planning settlement upgrading to monitoring expansion and infill. One method of gathering this information is through the analysis of nadir-view aerial imagery and the automated or semi-automated extraction of individual shacks. The problem of shack detection and delineation in, particularly South African, informal settlements is a unique and difficult one. This is primarily due to the inhomogeneous appearance of shack roofs, which are constructed from a variety of disparate materials, and the density of shacks. Previous research has focused mostly on the use of height data in conjunction with optical images to perform automated or semi-automated shack extraction. In this thesis, a novel approach to automating shack extraction is presented and prototyped, in which the appearance of shack roofs is homogenised, facilitating their detection. The main features of this strategy are: construction of an anisotropic scale-space from a single source image and detection of hypotheses at multiple scales; simplification of hypotheses' boundaries through discrete curve evolution and regularisation of boundaries in accordance with an assumed shack model - a 4-6 sided, compact, rectilinear shape; selection of hypotheses competing across scales using fuzzy rules; grouping of hypotheses based on their support for one another, and localisation and re-regularisation of boundaries through the incorporation of image edges. The prototype's performance is evaluated in terms of standard metrics and is analysed for four different images, having three different sets of imaging conditions, and containing well over a hundred shacks. Detection rates in terms of building counts vary from 83% to 100% and, in terms of roof area coverage, from 55% to 84%. These results, each derived from a single source image, compare favourably with those of existing shack detection systems, especially automated ones which make use of richer source data. Integrating this scale-space approach with height data offers the promise of even better results

Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

Recent advances in the availability of very high-resolution (VHR) satellite data together with efficient data acquisition and large area coverage have led to an upward trend in their applications for automatic 3-D building model reconstruction which require large-scale and frequent updates, such as disaster monitoring and urban management. Digital Surface Models (DSMs) generated from stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting in rough building shape representations. To handle 3-D building model reconstruction using such low-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs together with orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satellite imagery. The algorithm consists of multiple steps including building boundary extraction and decomposition, image-based roof type classification, and initial roof parameter computation which are prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM (nDSM) and to select the best one, a parameter optimization method based on exhaustive search is used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building block are intersected to reconstruct the 3-D model of connecting roofs. All corresponding experiments are conducted on a dataset including four different areas of Munich city containing 208 buildings with different degrees of complexity. The results are evaluated both qualitatively and quantitatively. According to the results, the proposed approach can reliably reconstruct 3-D building models, even the complex ones with several inner yards and multiple orientations. Furthermore, the proposed approach provides a high level of automation by limiting the number of primitive roof types and by performing automatic parameter initialization

An automatic building extraction and regularisation technique using LiDAR point cloud data and orthoimage

The development of robust and accurate methods for automatic building detection and regularisation using multisource data continues to be a challenge due to point cloud sparsity, high spectral variability, urban objects differences, surrounding complexity, and data misalignment. To address these challenges, constraints on object's size, height, area, and orientation are generally benefited which adversely affect the detection performance. Often the buildings either small in size, under shadows or partly occluded are ousted during elimination of superfluous objects. To overcome the limitations, a methodology is developed to extract and regularise the buildings using features from point cloud and orthoimagery. The building delineation process is carried out by identifying the candidate building regions and segmenting them into grids. Vegetation elimination, building detection and extraction of their partially occluded parts are achieved by synthesising the point cloud and image data. Finally, the detected buildings are regularised by exploiting the image lines in the building regularisation process. Detection and regularisation processes have been evaluated using the ISPRS benchmark and four Australian data sets which differ in point density (1 to 29 points/m2), building sizes, shadows, terrain, and vegetation. Results indicate that there is 83% to 93% per-area completeness with the correctness of above 95%, demonstrating the robustness of the approach. The absence of over- and many-to-many segmentation errors in the ISPRS data set indicate that the technique has higher per-object accuracy. While compared with six existing similar methods, the proposed detection and regularisation approach performs significantly better on more complex data sets (Australian) in contrast to the ISPRS benchmark, where it does better or equal to the counterparts. © 2016 by the authors

Building extraction from airborne laser scanning data : an analysis of the state of the art

This article provides an overview of building extraction approaches applied to Airborne Laser Scanning (ALS) data by examining elements used in original publications, such as data set area, accuracy measures, reference data for accuracy assessment, and the use of auxiliary data. We succinctly analyzed the most cited publication for each year between 1998 and 2014, resulting in 54 ISI-indexed articles and 14 non-ISI indexed publications. Based on this, we position some built-in features of ALS to create a comprehensive picture of the state of the art and the progress through the years. Our analyses revealed trends and remaining challenges that impact the community. The results show remaining deficiencies, such as inconsistent accuracy assessment measures, limitations of independent reference data sources for accuracy assessment, relatively few documented applications of the methods to wide area data sets, and the lack of transferability studies and measures. Finally, we predict some future trends and identify some gaps which existing approaches may not exhaustively cover. Despite these deficiencies, this comprehensive literature analysis demonstrates that ALS data is certainly a valuable source of spatial information for building extraction. When taking into account the short civilian history of ALS one can conclude that ALS has become well established in the scientific community and seems to become indispensable in many application fields.(VLID)174964

Creating 3D city models from satellite imagery for integrated assessment and forecasting of solar energy

Buildings are the most prominent component in the urban environment. The geometric identification of urban buildings plays an important role in a range of urban applications, including 3D representations of buildings, energy consumption analysis, sustainable development, urban planning, risk assessment, and change detection. In particular, 3D building models can provide a comprehensive assessment of surfaces exposed to solar radiation. However, the identification of the available surfaces on urban structures and the actual locations which receive a sufficient amount of sunlight to increase installed power capacity (e.g. Photovoltaic systems) are crucial considerations for solar energy supply efficiency. Although considerable research has been devoted to detecting the rooftops of buildings, less attention has been paid to creating and completing 3D models of urban buildings. Therefore, there is a need to increase our understanding of the solar energy potential of the surfaces of building envelopes so we can formulate future adaptive energy policies for improving the sustainability of cities. The goal of this thesis was to develop a new approach to automatically model existing buildings for the exploitation of solar energy potential within an urban environment. By investigating building footprints and heights based on shadow information derived from satellite images, 3D city models were generated. Footprints were detected using a two level segmentation process: (1) the iterative graph cuts approach for determining building regions and (2) the active contour method and the adjusted-geometry parameters method for modifying the edges and shapes of the extracted building footprints. Building heights were estimated based on the simulation of artificial shadow regions using identified building footprints and solar information in the image metadata at pre-defined height increments. The difference between the actual and simulated shadow regions at every height increment was computed using the Jaccard similarity coefficient. The 3D models at the first level of detail were then obtained by extruding the building footprints based on their heights by creating image voxels and using the marching cube approach. In conclusion, 3D models of buildings can be generated solely from 2D data of the buildings’attributes in any selected urban area. The approach outperforms the past attempts, and mean error is reduced by at least 21%. Qualitative evaluations of the study illustrate that it is possible to achieve 3D building models based on satellite images with a mean error of less than 5 m. This comprehensive study allows for 3D city models to be generated in the absence of elevation attributes and additional data. Experiments revealed that this novel, automated method can be useful in a number of spatial analyses and urban sustainability applications