3D Model Construction in an Urban Environment from Sparse LiDAR Points and Aerial Photos—a Statistical Approach

Light Detection and Ranging (LiDAR) has become an important data source in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated algorithms. The aerial photos, on the other hand, provide continuous spectral information on buildings. However, the accuracy of classified building boundaries from aerial photos is constrained when building roofs and their surroundings share analogous spectral characteristics. This paper develops a statistical approach that can integrate characteristic variables derived from sparse LiDAR points and air photos to detect buildings by estimating object heights and identifying clusters of similar heights. Within this study, the approach chooses a local regression method, namely geographically-weighted regression (GWR), to account for local variations of building surface height. In the GWR model, LiDAR data provide the height information of spatial objects, which is the dependent variable, while the brightness values from visible bands of the aerial photo serve as the independent variables. The established GWR model estimates the height at each pixel based on height values of its surrounding pixels with consideration of the distances between the pixels as well as similarities between their brightness values in visible bands. Clusters of contiguous pixels with higher estimated height val ues distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed statistical method is better than those by image classification of aerial photos alone or by building extraction of LiDAR data alone. The results demonstrate that this simple and effective method can be very useful for automatic detection of buildings in urban areas. The approach can be most helpful for studies of urban areas where more suitable but expensive high resolution data are not available.

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A Hybrid GWR-Based Height Estimation Method for Building Detection in Urban Environments

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-2-23-2014 ◽

2014 ◽

Vol II-2 ◽

pp. 23-29 ◽

Cited By ~ 2

Author(s):

X. Wei ◽

X. Yao

Keyword(s):

Urban Areas ◽

Estimation Method ◽

Urban Environments ◽

Lidar Data ◽

Building Detection ◽

Building Roof ◽

Aerial Photos ◽

The Road ◽

Urban Modelling ◽

Gwr Model

LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR model, LiDAR data provide the height information of spatial objects which is the dependent variable, while the brightness values from multiple bands of the aerial photo serve as the independent variables. The proposed method can thus estimate the height at each pixel from values of its surrounding pixels with consideration of the distances between the pixels and similarities between their brightness values. Clusters of contiguous pixels with higher estimated height values distinguish themselves from surrounding roads or other surfaces. A case study is conducted to evaluate the performance of the proposed method. It is found that the accuracy of the proposed hybrid method is better than those by image classification of aerial photos along or by height extraction of LiDAR data alone. We argue that this simple and effective method can be very useful for automatic detection of buildings in urban areas.

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Application of UAV-SfM photogrammetry and aerial LiDAR to a disastrous flood: multitemporal topographic measurement of a newly formed crevasse splay of the Kinu River, central Japan

10.5194/nhess-2017-42 ◽

2017 ◽

Cited By ~ 3

Author(s):

Atsuto Izumida ◽

Shoichiro Uchiyama ◽

Toshihiko Sugai

Keyword(s):

Urban Areas ◽

Lidar Data ◽

Residential Areas ◽

High Definition ◽

Central Japan ◽

Aerial Photos ◽

Ground Survey ◽

Disastrous Flood ◽

Aerial Vehicle ◽

Crevasse Splay

Abstract. Geomorphic impacts of a disastrous crevasse splay that formed in September 2015 and its post-formation modifications were quantitatively documented by using multitemporal, high-definition digital surface models (DSMs) of an inhabited and cultivated floodplain of the Kinu River, central Japan. The DSMs used were based on pre-flood (resolution, 2 m) and post-flood (resolution, 1 m) aerial light detection and ranging (LiDAR) data from January 2007 and September 2015, respectively, and structure-from-motion (SfM) photogrammetry data (resolution, 3.8 cm) derived from aerial photos taken by an unmanned aerial vehicle (UAV) in December 2015. After elimination of systematic errors among the DSMs, differential DSMs were produced by subtraction and topographic changes on the order of 10−1 m were detected. These changes were found to be consistent with previously reported ground survey data. The detected changes included not only topographic changes but also growth of vegetation, vanishing of floodwaters, and restoration and repair works carried out by people. The results suggest that DSMs with different resolutions and acquisition periods acquired by using a combination of UAV-SfM and LiDAR data can be used to quantify, rapidly and in rich detail, sudden topographic changes on floodplains caused by floods. Moreover, they have the great advantage that they can be used to archive such changes that occur in residential areas and urban areas where their preservation potential is low.

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LiDAR-Based System and Optical VHR Data for Building Detection and Mapping

Sensors ◽

10.3390/s20051285 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1285 ◽

Cited By ~ 3

Author(s):

Silvia Liberata Ullo ◽

Chiara Zarro ◽

Konrad Wojtowicz ◽

Giuseppe Meoli ◽

Mariano Focareta

Keyword(s):

Environmental Monitoring ◽

Case Studies ◽

Urban Areas ◽

Lidar Data ◽

Building Detection ◽

The Earth ◽

Areas Of Interest ◽

2D And 3D ◽

3D Lidar ◽

Very High

The aim of this paper is to highlight how the employment of Light Detection and Ranging (LiDAR) technique can enhance greatly the performance and reliability of many monitoring systems applied to the Earth Observation (EO) and Environmental Monitoring. A short presentation of LiDAR systems, underlying their peculiarities, is first given. References to some review papers are highlighted, as they can be regarded as useful guidelines for researchers interested in using LiDARs. Two case studies are then presented and discussed, based on the use of 2D and 3D LiDAR data. Some considerations are done on the performance achieved through the use of LiDAR data combined with data from other sources. The case studies show how the LiDAR-based systems, combined with optical Very High Resolution (VHR) data, succeed in improving the analysis and monitoring of specific areas of interest, specifically how LiDAR data help in exploring external environment and extracting building features from urban areas. Moreover the discussed Case Studies demonstrate that the use of the LiDAR data, even with a low density of points, allows the development of an automatic procedure for accurate building features extraction, through object-oriented classification techniques, therefore by underlying the importance that even simple LiDAR-based systems play in EO and Environmental Monitoring.

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A new expert system module for building detection in urban areas using spectral information and LIDAR data

Applied Geomatics ◽

10.1007/s12518-009-0013-1 ◽

2009 ◽

Vol 1 (4) ◽

pp. 97-110 ◽

Cited By ~ 6

Author(s):

Ayman Rashad Elshehaby ◽

Lamyaa Gamal El-deen Taha

Keyword(s):

Expert System ◽

Urban Areas ◽

Spectral Information ◽

Lidar Data ◽

Building Detection ◽

System Module

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Integrating edge/boundary priors with classification scores for building detection in very high resolution data

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2017.8127705 ◽

2017 ◽

Author(s):

Maria Vakalopoulou ◽

Norbert Bus ◽

Konstantinos Karantzalos ◽

Nikos Paragios

Keyword(s):

High Resolution ◽

Building Detection ◽

High Resolution Data ◽

Very High ◽

Resolution Data

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3D RECONSTRUCTION OF BUILDINGS WITH GABLED AND HIPPED STRUCTURES USING LIDAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w3-47-2014 ◽

2014 ◽

Vol XL-2/W3 ◽

pp. 47-52

Author(s):

H. Amini ◽

P. Pahlavani ◽

R. Karimi

Keyword(s):

Urban Areas ◽

3D Model ◽

Remote Sensing Data ◽

Training Data ◽

Aerial Images ◽

Lidar Data ◽

Building Detection ◽

3D Model Reconstruction ◽

Accuracy And Precision ◽

Intersection Line

Buildings are the most important objects in urban areas. Thus, building detection using photogrammetry and remote sensing data as well as 3D model of buildings are very useful for many applications such as mobile navigation, tourism, and disaster management. In this paper, an approach has been proposed for detecting buildings by LiDAR data and aerial images, as well as reconstructing 3D model of buildings. In this regard, firstly, building detection carried out by utilizing a Supper Vector Machine (SVM) as a supervise method. The supervise methods need training data that could be collected from some features. Hence, LiDAR data and aerial images were utilized to produce some features. The features were selected by considering their abilities for separating buildings from other objects. The evaluation results of building detection showed high accuracy and precision of the utilized approach. The detected buildings were labeled in order to reconstruct buildings, individually. Then the planes of each building were separated and adjacent planes were recognized to reduce the calculation volume and to increase the accuracy. Subsequently, the bottom planes of each building were detected in order to compute the corners of hipped roofs using intersection of three adjacent planes. Also, the corners of gabled roofs were computed by both calculating the intersection line of the adjacent planes and finding the intersection between the planes intersection line and their detected parcel. Finally, the coordinates of some nodes in building floor were computed and 3D model reconstruction was carried out. In order to evaluate the proposed method, 3D model of some buildings with different complexity level were generated. The evaluation results showed that the proposed method has reached credible performance.

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Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

Remote Sensing ◽

10.3390/rs11182105 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2105 ◽

Cited By ~ 1

Author(s):

Berninger ◽

Lohberger ◽

Zhang ◽

Siegert

Keyword(s):

High Resolution ◽

Tropical Forests ◽

Forest Degradation ◽

Canopy Height ◽

Carbon Accounting ◽

Lidar Data ◽

Above Ground Biomass ◽

High Resolution Data ◽

Ground Biomass ◽

Resolution Data

Globally available high-resolution information about canopy height and AGB is important for carbon accounting. The present study showed that Pol-InSAR data from TS-X and RS-2 could be used together with field inventories and high-resolution data such as drone or LiDAR data to support the carbon accounting in the context of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects.

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