Building Outline Research Articles

Comparison Study of Three Building Regularization Algorithms

Abstract. Building outline generation and regularization is an ongoing topic in remote sensing applications. The success of the methods used for building outline detection impacts studies that depend on the accuracy of the methods applied, such as urban planning, geospatial analysis, and 3D city modeling. The results of the building outline detection methods can vary due to several factors, such as the area to which they are applied and/or the parameters used for the methods. Since there are well-established deep-learning based software and plugins for building outlining, this paper compares the results of such a method, namely an open-source AI implementation (Mapflow), with the standard non-deep-learning based tools introduced by (Mousa et al., 2019) and (Bulatov et al., 2014). We present a comparative analysis of these two methods for regularizing building outlines in terms of accuracy, efficiency, and robustness in dealing with different levels of buildings’ complexity and structures. While the results of (Mousa et al., 2019) and (Bulatov et al., 2014) are comparable and outperform the AI method, (Mousa et al., 2019) is the method least impacted by the different parameters, however, has a higher computing time.

Contour Extraction of UAV Point Cloud Based on Neighborhood Geometric Features of Multi-Level Growth Plane

The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points.

Correcting the Location Error of Persistent Scatterers in an Urban Area Based on Adaptive Building Contours Matching: A Case Study of Changsha

Persistent Scatterer InSAR (PS-InSAR) technology enables the monitoring of displacement in millimeters. However, without the use of external parameter correction, radar scatterers exhibit poor geopositioning precision in meters, limiting the correlation between observed deformation and the actual structure. The integration of PS-InSAR datasets and building databases is often overlooked in deformation research. This paper presents a novel strategy for matching between PS points and building contours based on spatial distribution characteristics. A convex hull is employed to simplify the building outline. Considering the influence of building height and incident angle on geometric distortion, an adaptive buffer zone is established. The PS points on a building are further identified through the nearest neighbor method. In this study, both ascending and descending TerraSAR-X orbit datasets acquired between 2016 and 2019 were utilized for PS-InSAR monitoring. The efficacy of the proposed method was evaluated by comparing the PS-InSAR results obtained from different orbits. Through a process of comparison and verification, it was demonstrated that the matching effect between PS points and building contours was significantly enhanced, resulting in an increase of 29.2% in the number of matching PS points. The results indicate that this novel strategy can be employed to associate PS points with building outlines without the need for complex calculations, thereby providing a robust foundation for subsequent building risk assessment.

Regular Building Outline Extraction Based on Multi-Level Minimum Bounding Rectangle

Regular Building Outline Extraction Based on Multi-Level Minimum Bounding Rectangle

STUDY ON AUTOMATIC REGISTRATION METHOD OF SOURCE DATA FOR CIM BUILDING MODEL CONSTRUCTION

Abstract. The City Information Model (CIM) is a digital model designed to describe, analyze, and manage urban spatial information. It integrates various urban elements, providing decision support for urban planning, design, and management. This study focuses on the construction of building models in CIM, particularly the issue of automatic registration of source data. We propose an automatic registration method based on DWG-format architectural design drawings and SHAPE-format remote sensing building outline information. This method extracts the axis line information of the building from the plan design drawings, indirectly obtains feature points, and selects registration base points, thereby achieving the registration of real estate subdivision data. Meanwhile, by automatically fitting the outer bounding rectangle of the building and dynamically adjusting the angle, this study realizes the automatic registration of the aligned multilayer real estate subdivision data with the building outline data obtained from remote sensing images. This research provides an effective method for the automatic and batch accurate alignment of data for the construction of CIM building models, thereby enhancing the precision and reliability of the modeling.

Automatic building footprint extraction from photogrammetric and LiDAR point clouds using a novel improved-Octree approach

Extracting building footprints from optical data is a time-consuming process. Automatic extraction of building footprints from point clouds is a challenging problem in terms of geometric irregularities, noisy points, points density, and accuracy. The aim of this paper is to automatically extract and regularize building footprints using point clouds with a new approach called Improved Octree (I-Octree) by modifying the Octree method. The method consists of the separation of ground and above ground objects from the point cloud by Simple Morphological Filter (SMRF), the removing noisy points from point cloud with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, the automatic extraction of building footprints by I-Octree, and the regularization of the building footprints with Automatic Building Outline Regularization (ABORE) method. The proposed approach was implemented on photogrammetric and Light Detection and Ranging (LiDAR) in four test areas. Ground truth maps were utilized as reference data for accuracy analysis by using pixel-based accuracy method. The accuracy results were above 90 % for the photogrammetric point clouds and above 97 % for the LiDAR point cloud. It was proven that the proposed approach can extract and regularize the selected buildings with high accuracy compared the studies in literature. In conclusion, it was demonstrated that the proposed approach enables the automatic extraction and regularization of building footprints from point clouds. Consequently, the map production process with point cloud data is facilitated to be both more efficient and rapid, and the results confirm the high efficacy of the proposed approach.

Multilevel Regularization Method for Building Outlines Extracted from High-Resolution Remote Sensing Images

Extraction of vectorized building outlines from high-resolution remote sensing images is highly useful for various application fields, such as map creation and urban planning. However, this process is often complicated by external factors, such as trees and shadows, which cause issues, such as excessive node redundancy, jagged lines, and unclear corner points. In this study, a multilevel regularization method was designed for building outlines, including the “overall–local–detail” levels. First, overall regularization was performed by combining the minimum bounding rectangle of the building outline with the Hausdorff distance method. Next, based on the convex hull of the building outline and the distribution characteristics of nodes along the outline, the building outline was divided into multiple line chains and classified for local regularization. Finally, the details of the building outline were processed, with the parallel and perpendicular characteristics enhanced to obtain the final regularization results. The experimental results showed that the proposed method effectively enhances the edge representation accuracy of building outlines and significantly improves the accuracy and regularity of building edges. Furthermore, it strengthens the orthogonal characteristics of building outlines, providing more accurate representations of true building outlines.

3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model

Building point cloud completion is the process of reconstructing missing parts of a building’s point cloud, which have been affected by external factors during data collection, to restore the original geometric shape of the building. However, the uncertainty in filling point positions in the areas where building features are missing makes it challenging to recover the original distribution of the building’s point cloud shape. To address this issue, we propose a point cloud generation diffusion probability model based on building outline constraints. This method constructs building-outline-constrained regions using information related to the walls on the building’s surface and adjacent roofs. These constraints are encoded by an encoder and fused into latent codes representing the incomplete building point cloud shape. This ensures that the completed point cloud adheres closely to the real geometric shape of the building by constraining the generated points within the missing areas. The quantitative and qualitative results of the experiment clearly show that our method performs better than other methods in building point cloud completion.

Novel CNN-Based Approach for Reading Urban Form Data in 2D Images: An Application for Predicting Restaurant Location in Seoul, Korea

Artificial intelligence (AI) has demonstrated its ability to complete complex tasks in various fields. In urban studies, AI technology has been utilized in some limited domains, such as control of traffic and air quality. This study uses AI to better understand diverse urban studies data through a novel approach that uses a convolutional neural network (CNN). In this study, a building outline in the form of a two-dimensional image is used with its corresponding metadata to test the applicability of CNN in reading urban data. MobileNet, a high-efficiency CNN model, is trained to predict the location of restaurants in each building in Seoul, Korea. Consequently, using only 2D image data, the model satisfactorily predicts the locations of restaurants (AUC = 0.732); the model with 2D images and their metadata has higher performance but has an overfitting problem. In addition, the model using only 2D image data accurately predicts the regional distribution of restaurants and shows some typical urban forms with restaurants. The proposed model has several technical limitations but shows the potential to provide a further understanding of urban settings.

국공립 어린이집 그린리모델링 사업의 리트로핏 대안별 에너지 성능 및 경제성 비교 분석 : 단일 사례의 시뮬레이션 기반 분석 중심으로

Purpose: The purpose of this study was to present an optimized green remodeling retrofit plan through energy performance and economic analysis of the public daycare center for which green remodeling has been completed. Method: By collecting data on public building green remodeling project management in 2020 from the Korea Authority of Land & Infrastructure Safety, target daycare centers that are single-use and can collect monthly building energy consumption data through a public data open system were extracted. The building outline and operating conditions were investigated, and energy performance was analyzed using Design Builder, a dynamic building energy analysis program. After calibration, an alternative model analysis for each technical element was performed, including the basic base model. Then, economic analysis was performed using the net present value method. Result: The results of this study were as follows. After comprehensively analyzing energy performance and economic feasibility, ALT-4(heat recovery ventilation system), ALT-9 (heat recovery ventilation system+high-efficiency air conditioning+LED lighting replacement), ALT-6(LED lighting replacement) models were determined by the final priority. ALT-4(heat recovery ventilation system) as a single element model and ALT-9(heat recovery ventilation system+high-efficiency air conditioner+LED lighting replacement) as a combined element model were the best alternative when comprehensively considering energy performance and economic feasibility in green remodeling.

Multi-Task Edge Detection for Building Vectorization From Aerial Images

The extraction of building outline vectors is an essential task in supporting various applications. Although the recent development of deep learning based techniques has made advancements in the automation of this task, the accuracy and precision are insufficient due to errors caused by abundant noise and obstruction around buildings in aerial images. To better address this issue, this paper presents a new approach called the multi-task edge detection (MTED) for building vectorization with the following characteristics. First, instead of detecting building corner points that are very sensitive to noise effects, a deep learning based rotated bounding box detector is introduced for building edge detection to increase robustness to interference. Second, a multi-task learning strategy is designed to integrate building segmentation inside the METD framework to closely guide the edge detection using spatial context. Third, a simple yet effective geometry-guided post-processing method is designed to reconstruct vectorized building outlines based on the detected edges and learned building shape prior knowledge. The comparative experiments conducted on benchmark very-high-resolution optical aerial images indicate that the proposed approach can significantly outperform the state-of-the-arts in terms of vertex-based building outline accuracy metrics. With a test time of 58ms per building, this method enables efficient building polygon labeling in interactive mapping applications for building surveying and mapping. Code is available at https: //github.com/yifanthomaswu/MTED_framework.

Convolutional Neural Networks with Mask Shift Loss for Improving Building Outlines Detection

In the development of supervised semantic segmentation models for building outline detection, a significant challenge is obtaining sufficient accurately labelled training data. Old building outline datasets captured against historic imagery often exist covering the same locations as new imagery. By training Deep Convolutional Neural Networks to recognise buildings in these areas we can detect new and changed buildings. Ideally, historic building outlines as training labels paired with new orthophotography could be used to train models to detect buildings in new imagery. However, historic imagery may be orthorectified to a different standard, or have different off-nadir angles resulting in an apparent displacement of buildings between historic and current imagery sets and in labelled buildings that do not align accurately with the building image. This displacement problem presents difficulties in training accurate models. This paper proposes a novel loss function that searches the prediction segmentation masks for the best matching crop to a central crop of the training segmentation mask, thus reducing error due to linear displacements between an image and mask. According to the Intersection-over-Union metric, a ground truth test set for the building outlines detection problem improved from 0.751 to 0.802 on the Christchurch Buildings dataset when using the mask shift loss versus binary cross-entropy.

A Coarse-to-Fine DLG and TLS Data Registration Method

Terrestrial laser scanning (TLS) is an efficient tool to capture the point cloud for facade modeling. However, it is not easy to obtain the position by Global Navigation Satellite System (GNSS) as occluded by high buildings, which would lead to a problem for orienting the TLS data to a target coordinate system. In this paper, we propose a coarse-to-fine registration method for reliably orient the TLS data using digital line graph (DLG). The orientation is completed by horizontal plane registration and elevation registration part. The proposed method is composed of five steps: (1) Using the proposed method to extract the corner points from the building outline of DLG. (2) Using the proposed method based on RANSAC algorithm to extract the corner points and the façade point clouds of the TLS data. (3) Coarse alignment of the two extracted sets of corner points using 4PCS algorithm; (4) Fine registration by 2D-ICP algorithm; and (5) Completing elevation registration using elevation control points. Three data sets are employed to perform the experiments, and the results confirm that the proposed method is reliable and efficient in achieving the registration DLG and TLS data. By comparing to two other typical registration methods, the proposed method performs better.

A Recursive Hull and Signal-Based Building Footprint Generation from Airborne LiDAR Data

Automatically generating a building footprint from an airborne LiDAR point cloud is an active research topic because of its widespread usage in numerous applications. This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. In this workflow, LiDAR points that belong to the building category are first segmented into multiple clusters by applying the grid-based DBSCAN clustering algorithm. Each cluster contains the points of an individual building. Then, the outermost points of each building are extracted, on which the recursive convex hull algorithm is applied to generate the initial outline of each building. Since LiDAR points are irregularly distributed, the initial building outline contains irregular zig-zag shapes. In order to achieve a regularized building footprint that is close to the true building boundary, a signal-based regularization algorithm is developed. The initial outline is first transformed into a signal, which can reveal the wholistic geometric structure of the building outline after applying a denoising procedure. By analyzing the denoised signal, the locations of corners are identified, and the regularized building footprint is generated. The performance of the proposed workflow is tested and evaluated using two datasets that have different point densities and building types. The qualitative assessment reveals that the proposed workflow has a satisfying performance in generating building footprints even for building with complex structures. The quantitative assessment compares the performance of signal-based regularization with existing regularization methods using the 149 buildings contained in the test dataset. The experimental result shows the proposed method has achieved superior results based on a number of commonly used accuracy metrics.

Automatic building outline extraction from ALS point cloud data using generative adversarial network

The generation of building footprints from laser scanning point clouds or remote sensing images involves three steps: segmentation, outline extraction and boundary regularization/generalization. Currently, existing approaches mainly focus on the first and third steps, while only few studies have been conducted for the second step. However, the extraction result of the building outlines directly determines the regularization performance. Therefore, high-quality building outlines are important to be delivered for the regularization. Determining parameters, such as point distance and neighborhood radius, is the primary challenge in the process of extracting building outlines. In this study, a parameter-free method is proposed by using an improved generative adversarial network (GAN). It extracts building outlines from gridded binary images with default resolution and no other input of parameters. Hence, the parameter selection problem is overcome. The experimental results on segmented point cloud datasets of building roofs reveal that our method achieves the mean intersection over union of 93.52%, the Hausdorff distance of 0.640 m and the PoLiS of 0.165 m. The comparison with α-shape method shows that our method can improve the extraction performance of concave shapes and provide a more regularized outline result. The method reduces the difficulty and complexity of the next regularization task, and contributes to the accuracy of point cloud-based building footprint generation.

A Progressive Simplification Method for Buildings Based on Structural Subdivision

Building simplification is an important research area in automatic map generalization. Up to now, many approaches have been proposed by scholars. However, in the continuous transformation of scales for buildings, keeping the main shape characteristics, area, and orthogonality of buildings are always the key and difficult points. Therefore, this paper proposes a method of progressive simplification for buildings based on structural subdivision. In this paper, iterative simplification is adopted, which transforms the problem of building simplification into the simplification of the minimum details of building outlines. Firstly, a top priority structure (TPS) is determined, which represents the smallest detail in the outline of the building. Then, according to the orthogonality and concave–convex characteristics, the TPS are classified as 62 subdivisions, which cover the local structure of the building polygon. Then, the subdivisions are divided into four simplification types. The building is simplified to eliminate the TPS continuously, retaining the right-angle characteristics and area as much as possible, until the results satisfy the constraints and rules of simplification. A topographic dataset (1:1 K) collected from Kadaster was used for our experiments. In order to evaluate the algorithm, many tests were undertaken, including tests of multi-scale simplification and simplification of typical buildings, which indicate that this method can realize multi-scale presentation of buildings. Compared with the existing simplification methods, the comparison results show that the proposed method can simplify buildings effectively, which has certain advantages in keeping shape characteristics, area, and rectangularity.

Grid-based building outline extraction from ready-made building points

It is uncommon to use the grid network for extracting the building boundaries from building points, which is due to the effect of grid points on the outline accuracy. In this paper, a novel method is proposed for extracting the building outline from gridded building points. Accordingly, a grid is generated, and the boundary points are extracted. These points are traced based on a new topologically aware propagation algorithm. The initial principal directions are estimated first by using multiple points. Afterward, the initial outline is obtained. Next, the major part of our methodology starts with outline enlargement, which depends on the outline corners' propagation. The value and direction of the enlargement for each line are determined separately to generate an enlarged outline. The results show the high efficiency of the enlargement method, which improves the accuracy by up to 70%. The proposed method can achieve high pixel-based correctness of 99%.

Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery

High-resolution remote sensing image building target detection has wide application value in the fields of land planning, geographic monitoring, smart cities and other fields. However, due to the complex background of remote sensing imagery, some detailed features of building targets are less distinguishable from the background. When carrying out the detection task, it is prone to problems such as distortion and the missing of the building outline. To address this challenge, we developed a novel building target detection method. First, a building detection method based on rectangular approximation and region growth was proposed, and a saliency detection model based on the foreground compactness and local contrast of manifold ranking is used to obtain the saliency map of the building region. Then, the boundary prior saliency detection method based on the improved manifold ranking algorithm was proposed for the target area of buildings with low contrast with the background in remote sensing imagery. Finally, fusing the results of the rectangular approximation-based and saliency-based detection, the proposed fusion method improved the Recall and F1 value of building detection, indicating that this paper provides an effective and efficient high-resolution remote sensing image building target detection method.

A novel algorithm for regularization of building footprints using raw LiDAR point clouds

This study proposes an automatic building outline regularization approach (ABORE) that produces two-dimensional orthogonal building boundaries from irregular building footprint polygons with building edge points using raw LiDAR point cloud data. The proposed approach was evaluated on the combinations of single, double, and multiple buildings from seven LiDAR datasets with different point density. The accuracy of the building edge geometries regularized by the ABORE method was compared with the reference data without requiring any additional data. Test results showed that the mean completeness (Cp), correctness (Cr), and quality (Q) values for single buildings were estimated as 98.96, 99.32, 98.30% and for multiple buildings as 99.55, 99.74, 99.29%, respectively. These results indicate superior performance (by about 5% F-score improvement) in comparison to state-of-the-art methods with a higher correctness rate in building boundary regularization. ABORE was also found effective in the delineation of complex buildings with many corners and short edges.

Building Outline Extraction Directly Using the U2-Net Semantic Segmentation Model from High-Resolution Aerial Images and a Comparison Study

Deep learning techniques have greatly improved the efficiency and accuracy of building extraction using remote sensing images. However, high-quality building outline extraction results that can be applied to the field of surveying and mapping remain a significant challenge. In practice, most building extraction tasks are manually executed. Therefore, an automated procedure of a building outline with a precise position is required. In this study, we directly used the U2-net semantic segmentation model to extract the building outline. The extraction results showed that the U2-net model can provide the building outline with better accuracy and a more precise position than other models based on comparisons with semantic segmentation models (Segnet, U-Net, and FCN) and edge detection models (RCF, HED, and DexiNed) applied for two datasets (Nanjing and Wuhan University (WHU)). We also modified the binary cross-entropy loss function in the U2-net model into a multiclass cross-entropy loss function to directly generate the binary map with the building outline and background. We achieved a further refined outline of the building, thus showing that with the modified U2-net model, it is not necessary to use non-maximum suppression as a post-processing step, as in the other edge detection models, to refine the edge map. Moreover, the modified model is less affected by the sample imbalance problem. Finally, we created an image-to-image program to further validate the modified U2-net semantic segmentation model for building outline extraction.