Towards automatic modeling of buildings in informal settlements from aerial photographs using deformable active contour models (snakes)

Bibliography: leaves 177-187.This dissertation presents a novel system for semi-automatic modeling of buildings in informal settlement areas from aerial photographs. The building extraction strategy is developed and implememed with the aim of generatinga a desk top Informal Settlement Geographic lnformation System (ISGIS) using felf developed and available PC-based GIS tools to serve novice users informal settlement areas

Evaluation of automatic building detection approaches combining high resolution images and LiDAR data

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. © 2011 by the authors.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation and FEDER in the framework of the projects CGL2009-14220 and CGL2010-19591/BTE, and the support of the Spanish Instituto Geografico Nacional (IGN).Hermosilla, T.; Ruiz Fernández, LÁ.; Recio Recio, JA.; Estornell Cremades, J. (2011). Evaluation of automatic building detection approaches combining high resolution images and LiDAR data. Remote Sensing. 3:1188-1210. https://doi.org/10.3390/rs3061188S118812103Mayer, H. (1999). 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S., Shan, J., & Bethel, J. S. (2003). Class-Guided Building Extraction from Ikonos Imagery. Photogrammetric Engineering & Remote Sensing, 69(2), 143-150. doi:10.14358/pers.69.2.143STASSOPOULOU, A., & CAELLI, T. (2000). BUILDING DETECTION USING BAYESIAN NETWORKS. International Journal of Pattern Recognition and Artificial Intelligence, 14(06), 715-733. doi:10.1142/s0218001400000477Jin, X., & Davis, C. H. (2005). Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information. EURASIP Journal on Advances in Signal Processing, 2005(14). doi:10.1155/asp.2005.2196Kim, Z., & Nevatia, R. (1999). Uncertain Reasoning and Learning for Feature Grouping. Computer Vision and Image Understanding, 76(3), 278-288. doi:10.1006/cviu.1999.0803Dare, P. M. (2005). Shadow Analysis in High-Resolution Satellite Imagery of Urban Areas. 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Analysis of the factors affecting LiDAR DTM accuracy in a steep shrub area. International Journal of Digital Earth, 4(6), 521-538. doi:10.1080/17538947.2010.533201Ruiz, L. A., Recio, J. A., Fernández-Sarría, A., & Hermosilla, T. (2011). A feature extraction software tool for agricultural object-based image analysis. Computers and Electronics in Agriculture, 76(2), 284-296. doi:10.1016/j.compag.2011.02.007Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621. doi:10.1109/tsmc.1973.4309314Sutton, R. N., & Hall, E. L. (1972). Texture Measures for Automatic Classification of Pulmonary Disease. IEEE Transactions on Computers, C-21(7), 667-676. doi:10.1109/t-c.1972.223572Freund, Y. (1995). Boosting a Weak Learning Algorithm by Majority. Information and Computation, 121(2), 256-285. doi:10.1006/inco.1995.1136Shufelt, J. A. (1999). 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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

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

Automated Building Information Extraction and Evaluation from High-resolution Remotely Sensed Data

The two-dimensional (2D) footprints and three-dimensional (3D) structures of buildings are of great importance to city planning, natural disaster management, and virtual environmental simulation. As traditional manual methodologies for collecting 2D and 3D building information are often both time consuming and costly, automated methods are required for efficient large area mapping. It is challenging to extract building information from remotely sensed data, considering the complex nature of urban environments and their associated intricate building structures. Most 2D evaluation methods are focused on classification accuracy, while other dimensions of extraction accuracy are ignored. To assess 2D building extraction methods, a multi-criteria evaluation system has been designed. The proposed system consists of matched rate, shape similarity, and positional accuracy. Experimentation with four methods demonstrates that the proposed multi-criteria system is more comprehensive and effective, in comparison with traditional accuracy assessment metrics. Building height is critical for building 3D structure extraction. As data sources for height estimation, digital surface models (DSMs) that are derived from stereo images using existing software typically provide low accuracy results in terms of rooftop elevations. Therefore, a new image matching method is proposed by adding building footprint maps as constraints. Validation demonstrates that the proposed matching method can estimate building rooftop elevation with one third of the error encountered when using current commercial software. With an ideal input DSM, building height can be estimated by the elevation contrast inside and outside a building footprint. However, occlusions and shadows cause indistinct building edges in the DSMs generated from stereo images. Therefore, a “building-ground elevation difference model” (EDM) has been designed, which describes the trend of the elevation difference between a building and its neighbours, in order to find elevation values at bare ground. Experiments using this novel approach report that estimated building height with 1.5m residual, which out-performs conventional filtering methods. Finally, 3D buildings are digitally reconstructed and evaluated. Current 3D evaluation methods did not present the difference between 2D and 3D evaluation methods well; traditionally, wall accuracy is ignored. To address these problems, this thesis designs an evaluation system with three components: volume, surface, and point. As such, the resultant multi-criteria system provides an improved evaluation method for building reconstruction

An investigation into semi-automated 3D city modelling

Creating three dimensional digital representations of urban areas, also known as 3D city modelling, is essential in many applications, such as urban planning, radio frequency signal propagation, flight simulation and vehicle navigation, which are of increasing importance in modern society urban centres. The main aim of the thesis is the development of a semi-automated, innovative workflow for creating 3D city models using aerial photographs and LiDAR data collected from various airborne sensors. The complexity of this aim necessitates the development of an efficient and reliable way to progress from manually intensive operations to an increased level of automation. The proposed methodology exploits the combination of different datasets, also known as data fusion, to achieve reliable results in different study areas. Data fusion techniques are used to combine linear features, extracted from aerial photographs, with either LiDAR data or any other source available including Very Dense Digital Surface Models (VDDSMs). The research proposes a method which employs a semi automated technique for 3D city modelling by fusing LiDAR if available or VDDSMs with 3D linear features extracted from stereo pairs of photographs. The building detection and the generation of the building footprint is performed with the use of a plane fitting algorithm on the LiDAR or VDDSMs using conditions based on the slope of the roofs and the minimum size of the buildings. The initial building footprint is subsequently generalized using a simplification algorithm that enhances the orthogonality between the individual linear segments within a defined tolerance. The final refinement of the building outline is performed for each linear segment using the filtered stereo matched points with a least squares estimation. The digital reconstruction of the roof shapes is performed by implementing a least squares-plane fitting algorithm on the classified VDDSMs, which is restricted by the building outlines, the minimum size of the planes and the maximum height tolerance between adjacent 3D points. Subsequently neighbouring planes are merged using Boolean operations for generation of solid features. The results indicate very detailed building models. Various roof details such as dormers and chimneys are successfully reconstructed in most cases

An investigation into semi-automated 3D city modelling

Creating three dimensional digital representations of urban areas, also known as 3D city modelling, is essential in many applications, such as urban planning, radio frequency signal propagation, flight simulation and vehicle navigation, which are of increasing importance in modern society urban centres. The main aim of the thesis is the development of a semi-automated, innovative workflow for creating 3D city models using aerial photographs and LiDAR data collected from various airborne sensors. The complexity of this aim necessitates the development of an efficient and reliable way to progress from manually intensive operations to an increased level of automation. The proposed methodology exploits the combination of different datasets, also known as data fusion, to achieve reliable results in different study areas. Data fusion techniques are used to combine linear features, extracted from aerial photographs, with either LiDAR data or any other source available including Very Dense Digital Surface Models (VDDSMs). The research proposes a method which employs a semi automated technique for 3D city modelling by fusing LiDAR if available or VDDSMs with 3D linear features extracted from stereo pairs of photographs. The building detection and the generation of the building footprint is performed with the use of a plane fitting algorithm on the LiDAR or VDDSMs using conditions based on the slope of the roofs and the minimum size of the buildings. The initial building footprint is subsequently generalized using a simplification algorithm that enhances the orthogonality between the individual linear segments within a defined tolerance. The final refinement of the building outline is performed for each linear segment using the filtered stereo matched points with a least squares estimation. The digital reconstruction of the roof shapes is performed by implementing a least squares-plane fitting algorithm on the classified VDDSMs, which is restricted by the building outlines, the minimum size of the planes and the maximum height tolerance between adjacent 3D points. Subsequently neighbouring planes are merged using Boolean operations for generation of solid features. The results indicate very detailed building models. Various roof details such as dormers and chimneys are successfully reconstructed in most cases

Large-scale 3D environmental modelling and visualisation for flood hazard warning.

3D environment reconstruction has received great interest in recent years in areas such as city planning, virtual tourism and flood hazard warning. With the rapid development of computer technologies, it has become possible and necessary to develop new methodologies and techniques for real time simulation for virtual environments applications. This thesis proposes a novel dynamic simulation scheme for flood hazard warning. The work consists of three main parts: digital terrain modelling; 3D environmental reconstruction and system development; flood simulation models. The digital terrain model is constructed using real world measurement data of GIS, in terms of digital elevation data and satellite image data. An NTSP algorithm is proposed for very large data assessing, terrain modelling and visualisation. A pyramidal data arrangement structure is used for dealing with the requirements of terrain details with different resolutions. The 3D environmental reconstruction system is made up of environmental image segmentation for object identification, a new shape match method and an intelligent reconstruction system. The active contours-based multi-resolution vector-valued framework and the multi-seed region growing method are both used for extracting necessary objects from images. The shape match method is used with a template in the spatial domain for a 3D detailed small scale urban environment reconstruction. The intelligent reconstruction system is designed to recreate the whole model based on specific features of objects for large scale environment reconstruction. This study then proposes a new flood simulation scheme which is an important application of the 3D environmental reconstruction system. Two new flooding models have been developed. The first one is flood spreading model which is useful for large scale flood simulation. It consists of flooding image spatial segmentation, a water level calculation process, a standard gradient descent method for energy minimization, a flood region search and a merge process. The finite volume hydrodynamic model is built from shallow water equations which is useful for urban area flood simulation. The proposed 3D urban environment reconstruction system was tested on our simulation platform. The experiment results indicate that this method is capable of dealing with complicated and high resolution region reconstruction which is useful for many applications. When testing the 3D flood simulation system, the simulation results are very close to the real flood situation, and this method has faster speed and greater accuracy of simulating the inundation area in comparison to the conventional flood simulation model

Merging digital surface models sourced from multi-satellite imagery and their consequent application in automating 3D building modelling

Recently, especially within the last two decades, the demand for DSMs (Digital Surface Models) and 3D city models has increased dramatically. This has arisen due to the emergence of new applications beyond construction or analysis and consequently to a focus on accuracy and the cost. This thesis addresses two linked subjects: first improving the quality of the DSM by merging different source DSMs using a Bayesian approach; and second, extracting building footprints using approaches, including Bayesian approaches, and producing 3D models. Regarding the first topic, a probabilistic model has been generated based on the Bayesian approach in order to merge different source DSMs from different sensors. The Bayesian approach is specified to be ideal in the case when the data is limited and this can consequently be compensated by introducing the a priori. The implemented prior is based on the hypothesis that the building roof outlines are specified to be smooth, for that reason local entropy has been implemented in order to infer the a priori data. In addition to the a priori estimation, the quality of the DSMs is obtained by using field checkpoints from differential GNSS. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the Maximum Likelihood model which showed similar quantitative statistical results and better qualitative results. Perhaps it is worth mentioning that, although the DSMs used in the merging have been produced using satellite images, the model can be applied on any type of DSM. The second topic is building footprint extraction based on using satellite imagery. An efficient flow-line for automatic building footprint extraction and 3D model construction, from both stereo panchromatic and multispectral satellite imagery was developed. This flow-line has been applied in an area of different building types, with both hipped and sloped roofs. The flow line consisted of multi stages. First, data preparation, digital orthoimagery and DSMs are created from WorldView-1. Pleiades imagery is used to create a vegetation mask. The orthoimagery then undergoes binary classification into ‘foreground’ (including buildings, shadows, open-water, roads and trees) and ‘background’ (including grass, bare soil, and clay). From the foreground class, shadows and open water are removed after creating a shadow mask by thresholding the same orthoimagery. Likewise roads have been removed, for the time being, after interactively creating a mask using the orthoimagery. NDVI processing of the Pleiades imagery has been used to create a mask for removing the trees. An ‘edge map’ is produced using Canny edge detection to define the exact building boundary outlines, from enhanced orthoimagery. A normalised digital surface model (nDSM) is produced from the original DSM using smoothing and subtracting techniques. Second, start Building Detection and Extraction. Buildings can be detected, in part, in the nDSM as isolated relatively elevated ‘blobs’. These nDSM ‘blobs’ are uniquely labelled to identify rudimentary buildings. Each ‘blob’ is paired with its corresponding ‘foreground’ area from the orthoimagery. Each ‘foreground’ area is used as an initial building boundary, which is then vectorised and simplified. Some unnecessary details in the ‘edge map’, particularly on the roofs of the buildings can be removed using mathematical morphology. Some building edges are not detected in the ‘edge map’ due to low contrast in some parts of the orthoimagery. The ‘edge map’ is subsequently further improved also using mathematical morphology, leading to the ‘modified edge map’. Finally, A Bayesian approach is used to find the most probable coordinates of the building footprints, based on the ‘modified edge map’. The proposal that is made for the footprint a priori data is based on the creating a PDF which assumes that the probable footprint angle at the corner is 90o and along the edge is 180o, with a less probable value given to the other angles such as 45o and 135o. The 3D model is constructed by extracting the elevation of the buildings from the DSM and combining it with the regularized building boundary. Validation, both quantitatively and qualitatively has shown that the developed process and associated algorithms have successfully been able to extract building footprints and create 3D models

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