Building Boundary Extraction Research Articles

Pixels relationship analysis for extracting building footprints

Abstract. Currently, remote sensing research is focused on developing an automated algorithm that can compete with empirical methods for mapping the contours of individual buildings. Despite facing numerous challenges related to suboptimal imaging conditions, diverse building architecture, and complex backgrounds, creating such an algorithm is essential for monitoring urban and natural areas, generating 3D city models, managing disasters, and estimating population density. Obtaining the polygonal boundary of a building and extracting a vectorized building mask as output for direct use is one of the current challenges in drawing building outlines. This work provides a comprehensive workflow for building extraction and improves their predicted area by regularization the boundaries of buildings. First, a convolutional neural network is used to train the binary semantic segmentation model and then regularization and vectorization processes are performed. The main difference from existing methods is a new regularization method based on compiling a neighborhood matrix for each point belonging to the “building” class. According to the experimental results, the algorithm shows high efficiency: IOU (intersection over union) = 91.2%, AP (average precision) = 64.1% and AR (average recall) = 75.1%, comparable to leading building boundary extraction methods such as PolyWorld.

Deep-Learning-Based Automated Building Information Modeling Reconstruction Using Orthophotos with Digital Surface Models

In the field of building information modeling (BIM), converting existing buildings into BIM by using orthophotos with digital surface models (DSMs) is a critical technical challenge. Currently, the BIM reconstruction process is hampered by the inadequate accuracy of building boundary extraction when carried out using existing technology, leading to insufficient correctness in the final BIM reconstruction. To address this issue, this study proposes a novel deep-learning- and postprocessing-based approach to automating reconstruction in BIM by using orthophotos with DSMs. This approach aims to improve the efficiency and correctness of the reconstruction of existing buildings in BIM. The experimental results in the publicly available Tianjin and Urban 3D reconstruction datasets showed that this method was able to extract accurate and regularized building boundaries, and the correctness of the reconstructed BIM was 85.61% and 82.93%, respectively. This study improved the technique of extracting regularized building boundaries from orthophotos and DSMs and achieved significant results in enhancing the correctness of BIM reconstruction. These improvements are helpful for the reconstruction of existing buildings in BIM, and this study provides a solid foundation for future improvements to the algorithm.

EXTRACTION OF ORTHOGONAL BUILDING BOUNDARY FROM AIRBORNE LIDAR DATA BASED ON FEATURE DIMENSION REDUCTION

Abstract. Building boundary extraction is an active research topic in the field of feature extraction from airborne LiDAR point cloud data. Owing to the high complexity of most building extraction algorithms based on point clouds, multiple feature parameters must often be combined with iterative operations, particularly in the process of mitigating the sawtooth phenomenon using the sleeve algorithm and its improved versions. To improve the degree of automation and ensure accuracy, this study proposes a fast corner point detection method based on a dimensionality reduction technique, which utilizes reduced data mapping from 3D to 2D. We converted the boundaries extracted by the alpha shape algorithm to a 2D image and applied recursive Gaussian filtering with a relatively high level of automation to smoothen the image edges and mitigate the sawtooth phenomenon, thereby improving upon the sleeve algorithm, which requires a large number of iterations. Subsequently, the Douglas Peucker algorithm is used to retrieve the contour key points after extracting the contour lines and obtaining the regularized building contours using the grouped orthogonal regularization method. To verify the accuracy of the algorithm, it was compared with a cluster and adjustment (CAA)method based on the sleeve algorithm using three major evaluation metrics with respect to four representative building instances in two experimental datasets of urban areas. The value of the RMSE was reduced by an average of 43.79%. In addition, the time complexity decreased from O(n2) to O(n). These results demonstrate that the proposed method improves not only the accuracy of boundary extraction, but also the efficiency of data processing.

Regularized Building Boundary Extraction From Remote Sensing Imagery Based on Augment Feature Pyramid Network and Morphological Constraint

Regularized Building Boundary Extraction From Remote Sensing Imagery Based on Augment Feature Pyramid Network and Morphological Constraint

Automatic Building Boundary Extraction From Airborne LiDAR Data Robust to Density Variation

The alpha-shape ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice of the parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> . Despite several studies in the literature, the adaptive choice of the parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> persists a challenge in boundary extraction, especially when abrupt density variations occur. To overcome this limitation, this letter proposes a new approach combining five estimation strategies. In the proposed method, these strategies are tested sequentially, prioritizing the one that provides greater level of details. The experiments were conducted considering buildings with different characteristics, which were selected from two LiDAR data sets with the average point densities of 12 points/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 4 points/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The obtained results, presenting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {F} _{{\text {score}}}$ </tex-math></inline-formula> and PoLiS around 98% and 0.32 m, respectively, indicate the robustness of the proposed method even when abrupt density variation occurs.

BOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS

Abstract. In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to support a wide range of civil, public and military applications. One of the key information obtained from satellite imagery is to produce and update spatial maps of built environment due to its wide coverage with high resolution data. However, reconstructing spatial maps from satellite imagery is not a trivial vision task as it requires reconstructing a scene or object with high-level representation such as primitives. For the last decade, significant advancement in object detection and representation using visual data has been achieved, but the primitive-based object representation still remains as a challenging vision task. Thus, a high-quality spatial map is mainly produced through complex labour-intensive processes. In this paper, we propose a novel deep neural network, which enables to jointly detect building instance and regularize noisy building boundary shapes from a single satellite imagery. The proposed deep learning method consists of a two-stage object detection network to produce region of interest (RoI) features and a building boundary extraction network using graph models to learn geometric information of the polygon shapes. Extensive experiments show that our model can accomplish multi-tasks of object localization, recognition, semantic labelling and geometric shape extraction simultaneously. In terms of building extraction accuracy, computation efficiency and boundary regularization performance, our model outperforms the state-of-the-art baseline models.

An improved minimum bounding rectangle algorithm for regularized building boundary extraction from aerial LiDAR point clouds with partial occlusions

ABSTRACT Airborne Light Detection And Ranging (LiDAR) point cloud is an important data source for building 3D digital cities and can be used to acquire and update urban buildings, roads and etc. However, it is difficult to extract complete and accurate building boundaries from airborne LiDAR point clouds due to partial occlusion, which is mainly caused by adjacent tall trees, particularly in spring and summer. In this paper, we propose an improved minimum bounding rectangle (IMBR) algorithm to extract complete and accurate regularized building boundaries with and without partial occlusion from aerial LiDAR point clouds. The new algorithm only uses LiDAR point cloud and doesn’t need any additional data source. In addition, the algorithm can be applied to buildings with complex shapes. To test the proposed algorithm and compare it with the recursive minimum bounding rectangle (RMBR) algorithm, three datasets with different types of partial occlusions and different shapes were tested. The experimental results show that IMBR can successfully extract the complete and accurate regularized building boundary with or without partial occlusion, and its accuracy is equal to that of RMBR algorithm.

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.

Robust building boundary extraction method based on dual-scale feature classification and decision fusion with satellite image

ABSTRACT Building edge or boundary extraction is always one of the most important issues for remote sensing application. In order to accurately extract the edges or boundary of a building, there are usually two problems. Firstly, strong interference edges from backgrounds such as road, trees and others cannot be avoided. Secondly, it is more difficult for the lower contrast building edges to be detected. In order to address these two problems to a certain extent, a Robust Building Boundary Extraction method (DS-RBBE) is proposed in this paper, which is based on dual-scale sparse SVM classification and decision fusion. First, training samples are automatically selected by employing prior knowledge of main direction and linearity information. Next, a sparse SVM classifier is trained using the dual-scale local edge features of the training samples. And then, the trained sparse SVM is employed to classify all extracted edges. Finally, a dual-scale decision fusion strategy is performed for final building boundary extraction. In order to evaluate the performance of proposed method, the experiments are conducted on different types of build regions. The results are shown that the proposed method can efficiently extract the building edges and boundaries.

Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.

OBJECT BASED BUILDING EXTRACTION AND BUILDING PERIOD ESTIMATION FROM UNMANNED AERIAL VEHICLE DATA

Abstract. The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages; (i) Building boundary extraction with Object Based Image Analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86 % and the maximum difference is 13.2 %. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments.

Building boundary extraction using LiDAR data and images

Building boundary extraction using LiDAR data and images

Triangulation-Based Point Cloud Data Processing Technology Research and Application

With the "Digital Earth" concept being put forward, people are starting to focus on geospatial information technology. Traditional manual building modeling process is gradually eliminated by history due to cumbersome and inefficient work. With massive data storage and processing technologies emerging and improving, people begin to explore building point cloud data measured by laser radar technology and to use point cloud data processing software for further building boundary extraction. In the model boundary extraction process, the use of prototype with the model fit is a good, clear and easy programming algorithm and triangulation algorithm.

An improved building boundary extraction algorithm based on fusion of optical imagery and LIDAR data

This article presents a new method of automatic boundary extraction using LIDAR-optical fusion suited to handle diverse building shapes. This method makes full use of the complementary advantages of LIDAR data and optical imagery. Different building features are extracted from the two data sources and fused to form the final complete building boundaries. First, the points of each roof patch are detected from LIDAR point cloud. This process consists of four steps: filtering, building detection, wall point removal and roof patch detection. Second, initial building edges are extracted from optical imagery using an improved Canny detector constrained by edge location information derived from the LIDAR point cloud as edge buffer areas. Finally, the roof patch and initial edges are integrated by mathematical morphology to form the final complete building boundaries. All processes have no constraints or rules on building shapes. This method is fully data-driven and suitable for any building shape. LIDAR data and aerial images of complex geographical environments are used to test the method. These experimental results demonstrate that our method can automatically extract accurate boundaries for buildings with complex shapes, and also is highly robust in complex environments.

New methodologies for precise building boundary extraction from LiDAR data and high resolution image

PurposeLight Detection and Ranging (LiDAR) offers a fast and effective way to acquire DSM and extract ground objects such as building, trees and so on. However, it is difficult to extract sharp and precise building boundary from LiDAR data, because its ground sample distance (GSD) is often worse than that of high resolution image. Recently, fusion of LiDAR and high resolution image becomes a promising approach to extract precise boundary. To find the correct and precise boundary, the aim of this paper is to present a series of novel algorithms to improve the quality.Design/methodology/approachTo find the correct and precise boundary, this paper presents a series of novel algorithms to improve the quality. At first, a progressive algorithm is presented to register LiDAR data and images; second, a modified adaptive TIN algorithm is presented to filter ground point, where a region growth method is applied in the adaptive TIN algorithm; third, a novel criterion based on the density, connectivity and distribution of point cluster is developed to distinguish trees point; fourth, a novel method based on the height difference between neighbor points is employed to extract coarse boundaries; at last, a knowledge based rule is put forward to identify correct building boundary from parallel edges.FindingsThorough experiments, it is conducted that: the registration results are accurate and reliable; filtered ground points has good quality, without missing or redundancy; all tree clusters bigger than one grid are detected, and points of walls and edges are eliminated with the new criterion; detected edges exactly locate at real building boundaries, and statistics show the detection correctness is 98 percent, and the detection completeness is 95 percent.Originality/valueAll results prove that precise boundary can be extracted with fusion of LiDAR and high resolution image.

HOUSEDIFF: A MAP-BASED BUILDING CHANGE DETECTION FROM HIGH RESOLUTION SATELLITE IMAGERY USING GEOMETRIC OPTIMIZATION METHOD

Abstract. This paper presents a novel structural image analysis method based on geometric optimization techniques towards automatic building change detection. The aim of this method is to efficiently detect the changes of various buildings such as small houses and houses with complex roof in an urban area from high resolution satellite imagery by comparing with spatial database (maps). The previous research has indicated of the effectiveness of a map-based building change detection approach, and further investigation suggests the following three problems; (1) the large diversity of building types, roof shape, roof materials, illumination condition and shadow, (2) the difficulty of imagery and maps matching which normally leads to considerable position error, (3) the capacity of extracting various types of newly-built buildings. To solve these problems, we propose a new geometric optimization method which consists of the following two steps; (1) the building recognition based on a combinatorial optimization method for optimal building boundary extraction, (2) the newly-built building extraction based on an optimal building hypothesis search method. The experimental results showed that the detection rate was approximately 89% for existing and changed buildings, and approximately 83% for newly-built buildings. These results demonstrate the effectiveness of the proposed geometric optimization methods to integrate bottom-up and top-down analysis. By combining the locally detected image features with consideration of regional contexts from map, our method can achieve highly accurate building change detection in urban area. The method has been applied to a building change detection service named "HouseDiff" and succeeded in assisting users.

Extraction and Regularization of Various Building Boundaries with Complex Shapes Utilizing Distribution Characteristics of Airborne LIDAR Points

This study presents an approach for extracting boundaries of various buildings, which have concave boundaries, inner yards, non-right-angled corners, and nonlinear edges. The approach comprises four steps: building point segmentation, boundary tracing, boundary grouping, and regularization. In the second and third steps, conventional algorithms are improved for more accurate boundary extraction, and in the final step, a new algorithm is presented to extract nonlinear edges. The unique characteristics of airborne light detection and ranging (LIDAR) data are considered in some steps. The performance and practicality of the presented algorithm were evaluated for buildings of various shapes, and the average omission and commission error of building polygon areas were 0.038 and 0.033, respectively.

Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.