Building Height Estimation Research Articles

Shadow Pattern-Enhanced Building Height Extraction Using Very-High-Resolution Image

Building height is valuable for a variety of foci in urban studies. The traditional field investigations are not practical for the updates of massive building height in a large-scale urban area. Given the relationship between building structures and their shadow sizes, the building shadow becomes practical for estimating its corresponding building height when its geometrical shape is visible in newly emerging very-high-resolution (VHR) images. However, the shadow shape of different buildings might vary significantly, posing a great challenge to determining the edge of shadow useful for predicting building height. This study proposes a shadow pattern classification system ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShadowClass</i> ) to summarize the varied shadow shapes into a number of pattern categories and employ a cutting-edge CNN model to classify the extracted shadows into a pattern for automatically determining the edge of a building shadow being useful for building height estimation. We integrated the proposed approach into two branches of the state-of-the-art approaches: shadow-based building height estimation with open cyberinfrastructure and shadow-based building height estimation with VHR image. The experimental results proved that the proposed method could be a practical solution for single and isolated buildings that have their complete shadow shape.

A Machine Learning Method for Building Height Estimation Based on Sentinel-2 Bi-Temporal Images

Building height information is essential for many applications such as urban planning and population density estimation. The building shadow length varies according to seasons, which is shown as different digital number values in multi-temporal images. Thus, the bi-temporal satellite remote sensing images of Sentinel-2 are used to estimate the buildings height in this study. An area of 15 km × 15 km in Beijing, China is taken as the study area. By preprocessing the data, the remaining pixels are split into two parts: 70% as the training data set and the rest as the testing data set. Then, one classification model and three regression models are proposed with using Random Forest (RF) method. Based on the testing data, it shows that the accuracy rate of the classification model has reached 98.4% with the kappa coefficient of 0.93. And the regression models' root-mean-square error (RMSE) is 0.61 floor for 1–6 floors group, 0.41 floor for 7–12 floor group, and 0.98 floor for above 12 floor group. The final RMSE is 1.62 floor with RF models. In general, this study shows the feasibility of using satellite mid-resolution optical image to estimate the building height and provides an important reference for regional building height estimation in the future.

SAR Tomography Based on Atomic Norm Minimization in Urban Areas

Synthetic aperture radar (SAR) tomography (TomoSAR) is a powerful tool for the three-dimensional (3D) reconstruction of buildings in urban areas. At present, the compressed sensing (CS) technique has been widely used in the TomoSAR inversion of urban areas because of the sparsity of the backscattering power of buildings along the elevation direction. However, this algorithm discretizes the elevation and assumes that the scatterers are located on predetermined finite grids. In fact, scatterers can lie anywhere in the elevation direction, regardless of grid point constraints. The phenomenon of scatterer positioning errors due to elevation discretization is called the off-grid effect, which will affect the height estimation accuracy of TomoSAR. To overcome this problem, we proposed a TomoSAR reconstruction algorithm based on atomic norm minimization (Tomo-ANM) in this paper. Tomo-ANM employs ANM, a continuous compressed sensing technique, to obtain scatterer positions on the continuous dictionary, thus eliminating the off-grid effect. Baseline compensation is necessary to obtain the data of virtual uniform baselines or the samples of uniform data during preprocessing. A fast realization of ANM, IVDST, is utilized to accelerate the process. Tomo-ANM was tested through simulation experiments, and the results confirmed the validity of eliminating the influence of off-grid effects and exhibited an improved location accuracy and detection rate in less time compared with the on-grid TomoSAR algorithm SL1MMER. Real data experiments based on eight staring spotlight TerraSAR-X images showed that Tomo-ANM can improve the accuracy of building height estimation by 4.83% relative to its real height.

Developing a data-fusing method for mapping fine-scale urban three-dimensional building structure

Understanding urban morphology is essential for various urban management studies and local environmental issues and guiding sustainable city development. Existing studies mainly focus on analyzing urban morphology from the horizontal aspect, while the urban vertical structure has rarely been discussed due to the scarcity of reliable and fine-scale urban three-dimensional (3-D) building data. This study develops an effective data-fusing methodology to estimate the heights of individual buildings at a city scale. We examined a machine-learning regression model by collecting public materials, including multi-source remote sensing-(RS)-based products, building-derived features, and relevant data to verify its performance in building height estimation. By applying the model in Shenzhen City, a dense city in the Guangdong-Hong Kong-Macao Greater Bay Area, results demonstrated that integrating rich multi-source explanatory variables could achieve high-accuracy building height retrieval. Using multiple building morphological metrics derived by building height data as proxy measures, the urban 3-D form patterns were further analyzed to understand current heterogeneous urban morphological structures. The proposed methodology can be conveniently applied to worldwide cities for urban 3-D morphology retrieval. Also, the available building height information is useful for planners to design morphological control for cities and thus contributes to sustainable and smart city development.

Building Height Extraction from GF-7 Satellite Images Based on Roof Contour Constrained Stereo Matching

Building height is one of the basic geographic information for planning and analysis in urban construction. It is still very challenging to estimate the accurate height of complex buildings from satellite images, especially for buildings with podium. This paper proposes a solution for building height estimation from GF-7 satellite images by using a roof contour constrained stereo matching algorithm and DSM (Digital Surface Model) based bottom elevation estimation. First, an object-oriented roof matching algorithm is proposed based on building contour to extract accurate building roof elevation from GF-7 stereo image, and DSM generated from the GF-7 stereo images is then used to obtain building bottom elevation. Second, roof contour constrained stereo matching is conducted between backward and forward image blocks, in which the difference of standard deviation maps is used for the similarity measure. To deal with the multi-height problem of podium buildings, the gray difference image is adopted to segment podium buildings, and re-matching is conducted to find out their actual heights. Third, the building height is obtained through the elevation difference between the building top and bottom, in which the evaluation of the building bottom is calculated according to the elevation histogram statistics of the building buffer in DSM. Finally, two GF-7 stereo satellite images, collected in Yingde, Guangzhou, and Xi’an, Shanxi, are used for performance evaluation. Besides, the aerial LiDAR point cloud is used for absolute accuracy evaluation. The results demonstrate that compared with other methods, our solution obviously improves the accuracy of height estimation of high-rise buildings. The MAE (Mean Absolute Error) of the estimated building heights in Yingde is 2.31 m, and the MAE of the estimated elevation of building top and bottom is approximately 1.57 m and 1.91 m, respectively. Then the RMSE (Root Mean Square Error) of building top and bottom is 2.01 m and 2.57 m. As for the Xi’an dataset with 7 buildings with podium out of 40 buildings, the MAE of the estimated building height is 1.69 m and the RMSE is 2.34 m. The proposed method can be an effective solution for building height extraction from GF-7 satellite images.

A building height dataset across China in 2017 estimated by the spatially-informed approach

As a fundamental aspect of the urban form, building height is a key attribute for reflecting human activities and human-environment interactions in the urban context. However, openly accessible building height maps covering the whole China remain sorely limited, particularly for spatially informed data. Here we developed a 1 km × 1 km resolution building height dataset across China in 2017 using Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data. Building height estimation was performed using the spatially-explicit Gaussian process regression (GPR) in 39 major Chinese cities where the spatially explicit and robust cadastral data are available and the spatially-implicit GPR for the remaining 304 cities, respectively. The cross-validation results indicated that the proposed Si-GPR model overall achieved considerable estimation accuracy (R2 = 0.81, RMSE = 4.22 m) across the entire country. Because of the implementation of local modelling, the spatially-explicit GPR outperformed (R2 = 0.89, RMSE = 2.82 m) the spatially-implicit GPR (R2 = 0.72, RMSE = 6.46 m) for all low-rise, mid-rise, and high-rise buildings. This dataset, with extensive-coverage and high-accuracy, can support further studies on the characteristics, causes, and consequences of urbanization.

Estimating building height in China from ALOS AW3D30

Building height plays an essential role in urban remote sensing and is of great significance for understanding the functioning of urban systems. However, there is little information about building height in China at the country-scale. Previous studies have employed a digital surface model (DSM) to map building height, but the slope correction for buildings in sloping areas remains understudied. In this context, we developed a method to estimate building height for all of China based on the Advanced Land Observing Satellite (ALOS) World 3D-30 m (AW3D30) DSM and other ancillary data including the Global Artificial Impervious Area (GAIA) dataset, the NASADEM dataset and the Global Roads Inventory Project (GRIP) dataset. The proposed method enabled us to accurately estimate building height with a special slope correction algorithm, improving the accuracy of building height estimation. The outcome of our procedure is a map of building height for China at a spatial resolution of 30 m. Compared to field-measured building height data and reference building height data from Baidu map, results indicate that the proposed method performed well (root mean square error (RMSE) of 4.26 m and 4.98 m, respectively). Moreover, the new building height product further demonstrates that our method alleviates the overestimation effect of buildings with small footprint area in the suburbs when compared with the existing building height product. Overall, the new building height map of China contributes to the improved management of urban areas and further studies of urban environments.

Scalable Building Height Estimation From Street Scene Images

Building height estimation plays an essential role in many applications such as 3D city rendering, urban planning, and navigation. Recently, a new building height estimation method was proposed using street scene images and 2D maps, which is more scalable than traditional methods that use high-resolution optical images, RADAR, or LiDAR data, which are proprietary or expensive to obtain. The method needs to detect building rooflines to compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images where buildings occlude each other or are blocked by other objects such as trees since rooflines can be difficult to locate. To address these limitations, we propose a robust building height estimation method that computes building height simultaneously from street scene images with an orientation along the street and images facing the building with an upward-looking view. We first detect roofline candidates from both types of images. Then, we use a deep neural network called RoofNet to classify and filter these candidates and select the best candidate via an entropy-based ranking algorithm. When the true roofline is identified, we compute building height via the pinhole camera model. Experimental results show that the proposed RoofNet model yields a higher accuracy on building corner and roofline candidate filtering compared with state-of-the-art open-set classifiers. Our overall building height estimation method outperforms the baseline by up to 11.9% in accuracy and achieves 92.8% in height estimation error within 4 meters on the collected data set.

Retrieving building height in urban areas using ICESat-2 photon-counting LiDAR data

Building height is a pivotal factor in studying urban form and understanding the impacts of the vertical characteristics of urban areas on the environment. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) employ photon-counting LiDAR to collect Earth’s surface elevation data globally. However, method at ground and building photon classification and subsequently building height extraction from photon-counting LiDAR data is still missing. This study aimed to develop a methodological framework to retrieve building height from ICESat-2 data under various data acquisition conditions stratified by day/night condition, strong/weak beam. First, the noise removal algorithm adopted by ATL03 product is utilized to filter out noise photons and retain signal photons in the raw dataset. Second, the random sample consensus (RANSAC) algorithm and statistical method are adopted to classify the signal photons into ground and building photons according to their characteristics and spatial neighborhood relationship. The building height is then computed by the elevation of building and ground photons. Finally, estimated heights are evaluated using the reference building height derived from terrestrial laser scanning (TLS) data. The results indicate that the proposed methodological framework can effectively identify building and ground photons under different building types. Strong consistence between estimated and reference building height is obtained by quantifying the estimation precision of building height under different data acquisition conditions, root mean square error (RMSE) ranging between 0.35 m and 0.45 m, indicating that the ATL03 data can be utilized to derive the building height in urban areas for all acquisition times and beam intensity. Further analysis demonstrates that strong/weak beam significantly influences the building height estimation compared to day/night condition. Overall, this study provides a method for estimating building height in urban areas using ICESat-2 data, and the findings approve the strong capability of ICESat-2 data in estimating building height.

Multi-Scene Building Height Estimation Method Based on Shadow in High Resolution Imagery

Accurately building height estimation from remote sensing imagery is an important and challenging task. However, the existing shadow-based building height estimation methods have large errors due to the complex environment in remote sensing imagery. In this paper, we propose a multi-scene building height estimation method based on shadow in high resolution imagery. First, the shadow of building is classified and described by analyzing the features of building shadow in remote sensing imagery. Second, a variety of shadow-based building height estimation models is established in different scenes. In addition, a method of shadow regularization extraction is proposed, which can solve the problem of mutual adhesion shadows in dense building areas effectively. Finally, we propose a method for shadow length calculation combines with the fish net and the pauta criterion, which means that the large error caused by the complex shape of building shadow can be avoided. Multi-scene areas are selected for experimental analysis to prove the validity of our method. The experiment results show that the accuracy rate is as high as 96% within 2 m of absolute error of our method. In addition, we compared our proposed approach with the existing methods, and the results show that the absolute error of our method are reduced by 1.24 m–3.76 m, which can achieve high-precision estimation of building height.

A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas: A case study of 42 Chinese cities

Knowledge of building height is critical for understanding the urban development process. High-resolution optical satellite images can provide fine spatial details within urban areas, while they have not been applied to building height estimation over multiple cities and the feasibility of mapping building height at a fine scale (< 5 m) remains understudied. Multi-view satellite images can describe vertical information of buildings, due to the inconsistent response of buildings (e.g., spectral and structural variations) to different viewing angles, but they have not been employed to deep learning-based building height estimation. In this context, we introduce high-resolution ZY-3 multi-view images to estimate building height at a spatial resolution of 2.5 m. We propose a multi-spectral, multi-view, and multi-task deep network (called M3Net) for building height estimation, where ZY-3 multi-spectral and multi-view images are fused in a multi-task learning framework. A random forest (RF) method using multi-source features is also carried out for comparison. We select 42 Chinese cities with diverse building types to test the proposed method. Results show that the M3Net obtains a lower root mean square error (RMSE) than the RF, and the inclusion of ZY-3 multi-view images can significantly lower the uncertainty of building height prediction. Comparison with two existing state-of-the-art studies further confirms the superiority of our method, especially the efficacy of the M3Net in alleviating the saturation effect of high-rise building height estimation. Compared to the vanilla single/multi-task models, the M3Net also achieves a lower RMSE. Moreover, the spatial-temporal transferability test indicates the robustness of the M3Net to imaging conditions and building styles. The test of our method on a relatively large area (covering about 14,120 km2) further validates the scalability of our method from the perspectives of both efficacy and quality. The source code will be made available at https://github.com/lauraset/BuildingHeightModel.

An approach to urban building height and floor estimation by using LiDAR data

Assessment of precise, fast, and up-to-date building and building floor data are essential for urban design, planning, and management and urban environmental studies. These maps are frequently required for tracking building construction speed, monitoring horizontal and vertical urban growth and illegal constructions, updating building records, preparing reasonable urban plans, assessing hazard and risk, and generating infrastructure plans. In this study, a combination of aerial photographs and LiDAR data are used to produce individual building heights and then estimate building the floor. The two recorded laser pulse reflections of LiDAR point cloud data: First pulse (FP) and last pulse (LP) reflections were used to obtain the digital surface model (DSM) and the digital terrain model (DTM), respectively. The normalized digital surface model (nDSM) is calculated to obtain the height values of each building. The accuracy of the proposed algorithm is assessed for each building floor using ground truth data and has shown an overall accuracy of 79% and a kappa equal to 0.74 which is a promising result.

IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery

Estimation of the Digital Surface Model (DSM) and building heights from single-view aerial imagery is a challenging inherently ill-posed problem that we address in this paper by resorting to machine learning. We propose an end-to-end trainable convolutional-deconvolutional deep neural network architecture that enables learning mapping from a single aerial imagery to a DSM for analysis of urban scenes. We perform multisensor fusion of aerial optical and aerial light detection and ranging (Lidar) data to prepare the training data for our pipeline. The dataset quality is key to successful estimation performance. Typically, a substantial amount of misregistration artifacts are present due to georeferencing/projection errors, sensor calibration inaccuracies, and scene changes between acquisitions. To overcome these issues, we propose a registration procedure to improve Lidar and optical data alignment that relies on Mutual Information, followed by Hough transform-based validation step to adjust misregistered image patches. We validate our building height estimation model on a high-resolution dataset captured over central Dublin, Ireland: Lidar point cloud of 2015 and optical aerial images from 2017. These data allow us to validate the proposed registration procedure and perform 3D model reconstruction from single-view aerial imagery. We also report state-of-the-art performance of our proposed architecture on several popular DSM estimation datasets.

A Study on the Building Height Estimation and Accuracy Using Unmanned Aerial Vehicles

A Study on the Building Height Estimation and Accuracy Using Unmanned Aerial Vehicles

Extraction of Multiple Building Heights Using ICESat/GLAS Full-Waveform Data Assisted by Optical Imagery

Although the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimetry System (ICESat/GLAS) has been used for urban monitoring, however, previous studies focused on extracting the maximum building height within the footprint. In fact, the full-waveform recording of GLAS data makes it possible to extract multiple building heights. However, the uncertainty of the spatial distribution and reflectance creates considerable challenges for the fine inversion of multiple building heights within the footprint. In this letter, we proposed an inversion method of multiple building heights using GLAS data assisted by QuickBird imagery. First, the GLAS waveform and the auxiliary optical imagery were processed to extract some spectral, horizontal, and vertical information as the prior knowledge of the inversion model. Then, the multiple building heights were inversed from the optimal simulated waveform based on the 3-D geometric optical and radiative transfer (GORT) model. The building heights measured by airborne LiDAR were used to validate the inversed building heights. The results demonstrated that the proposed inversion method achieved the building height estimation accurately and precisely ( $R^{2} = 0.971$ , rRMSE = 13.2%, and $n = 430$ ). This letter may shed some light on extracting multiple-level heights within the footprint using satellite LiDAR full-waveform data.

Estimation of building shape by block size

Abstract. Block restructuring has been strongly emphasized in Japan for renovating cities. However, little is known about the relation between block size and building shape. Moreover, the shape of buildings designed on a block after restructuring is unclear. Some estimation methods for urban physical status, such as building footprint location, floor area, and land use, have been developed in previous research. Taima et al. (2016) developed a model to estimate the building footprint area by using GIS. The future image of the building footprint on various blocks is visualized. Similarly, Asami and Ohtaki (2000) developed a model to estimate detached house location. Orford (2010) developed a methodology for estimating the floor area of individual properties from digital infrastructure data. Shiravi et al. (2015) assessed the utility of some models for estimating floor area using three data sources: a geographic vector building footprint layer, a LiDAR data set, and field survey data for the south side of the city of Fredericton, Canada. They discussed the reliability and accuracy of each model. In other research, Brunner et al. (2009) extended a methodology for building height estimation and tried to improve its accuracy. Schmidt et al. (2010) presented an approach to the estimation of building density on the block scale. Land use (Debnath and Amin, 2016; Jiang and Liu, 2012) and floor area (Orford, 2010) are popular topics and estimated in previous studies of the urban field, but estimation of building shape has seldom been a focus in the literature. Three-dimensional estimations of buildings cannot be found. If software to estimate building shape by block shape and other conditions was developed, it would be useful to determine urban planning, such as population estimation and landuse estimation. In this study, an estimation model is developed and applied to certain areas. In this study, the relation between block size and building shape is analyzed quantitatively, and a three-dimensional building shape is estimated by a model using an urban planning GIS data set of Tokyo (Figure 1 and 2). Results show the quantitative relation between block size and building shape, and the building shape image on the blocks. Higher buildings and buildings with a basement tend to be built in larger blocks, leading to efficient use of the maximum volume permitted in the block. In addition, the region composed by larger blocks can be spacious, because the range of building setback will be long in larger blocks. Designation of a high floor area ratio may induce integration and enlargement of blocks. Blocks are less likely to be partitioned in zones when a high floor area ratio is designated.

Monitoring 3D Building Change and Urban Redevelopment Patterns in Inner City Areas of Chinese Megacities Using Multi-View Satellite Imagery

Inner-city redevelopment is regarded as an effective way to promote land-use efficiency and optimize land-use structure, especially with the establishment of urban growth boundaries in Chinese cities. However, inner-city redevelopment as compared to urban sprawl has been rarely monitored in 2D space, let alone in 3D space. Therefore, in this paper, a novel approach to generate time-series 3D building maps (i.e., building footprint and height) based on high-resolution (2 m) multi-view ZY-3 satellite imagery was proposed. In the proposed method, the building footprint was updated by an object-based image-to-map change detection method, which employed spectral (i.e., HSV and NDVI) and structural features (i.e., morphological building index) to extract non-building and building objects, respectively; building height was estimated automatically through semi-global matching of multi-view images. We applied the proposed method to four representative Chinese megacities, i.e., Beijing, Xi’an, Shanghai, and Wuhan, for the period 2012–2017, and detected building footprints with overall accuracies ranging from 84.84% to 97.60%. The building height estimation was also relatively accurate, with the bias, slope, and root-mean-square error being −0.49–2.30 m, 0.93–1.10 m, and 4.94–7.31 m, respectively. Our results show that the total building coverage decreased over the study period, accompanied by an increase in both area-weighted building height and floor area ratio. In addition, compact low-rise buildings have been replaced by open high-rise buildings in the urban redevelopment process. Moreover, due to the scattered spatial distribution of the redevelopment sites, the local spatial aggregation patterns of building density are unlikely to shift between hotspots (i.e., spatial aggregation of high values) and coldspots (i.e., spatial aggregation of low values).

Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach

The increasing availability of open geospatial data, such as building footprint vector data and LiDAR (Light Detection and Ranging) point clouds, has provided opportunities to generate large-scale 3D city models at low cost. However, using unclassified point clouds with building footprints to estimate building heights may yield erroneous results due to potential errors and anomalies in both datasets and their integration. Some of the points within footprints often reflect irrelevant objects other than roofs, leading to biases in height estimation, and few studies have developed systematic methods to filter them out. In this paper, a LiDAR point classification methodology is proposed that extracts only rooftop points for building height estimation. The LiDAR points are characterized by point, footprint, and neighborhood-based features and classified by the Random Forest (RF) algorithm into four classes – rooftop, wall, ground, and high outlier. The percentage of correctly classified points among 15,577 sample points in Columbus, Ohio, amounts to 96.5%. Conducting this classification separately for different building types (commercial, residential, skyscraper, and small constructions) does not significantly change the overall accuracy. The footprint-based features contribute most to predicting the classes correctly. Height validation results based on a sample of 498 buildings show that (1) using average or median heights with classified points provides the best estimates, minimizing the disparities between computed heights and ground truth and (2) the RF method yields outcomes much closer to ground truth than earlier classification approaches. Some outcomes are visualized in 3D format using Google Earth 3D Imagery and ArcScene.

3D Virtual Urban Scene Reconstruction From a Single Optical Remote Sensing Image

This paper presents a low-cost and efficient method for 3D virtual urban scene reconstruction based on multi-source remote sensing big data and deep learning. By integrating maps, satellite optical images, and digital terrain model (DTM), the proposed method achieves a reasonable reconstructed 3D model for complex urban. The method consists of two independent convolutional neural networks (CNN) to process the land cover and the building height extraction. The proposed method is then tested on a 100 km 2 scene in San Diego, USA, including about 30 000 buildings. The land cover classification achieves an overall accuracy (OA) of 80.4% for eight types of land as defined in NLCD 2011 datasets. Building height estimation achieves an average error at 1.9 meters on NYC open data, the building footprint. Furthermore, the scene reconstruction including the estimation of both land cover and building height can be finished in 10 min on a single NVidia Titan X GPU.

Building Height Estimation from GF-2 Image based on Morphological Shadow Index

Building Height Estimation from GF-2 Image based on Morphological Shadow Index