Three-dimensional building roof boundary extraction using high-resolution aerial image and LiDAR data

2014 ◽  
Author(s):  
A. P. Dal Poz ◽  
Antonio J. Fazan
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
Aerial Image ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Building Roof

scholarly journals Snake-Based Model for Automatic Roof Boundary Extraction in the Object Space Integrating a High-Resolution Aerial Images Stereo Pair and 3D Roof Models

2021 ◽  
Vol 13 (8) ◽  
pp. 1429
Author(s):  
Michelle S. Y. Ywata ◽  
Aluir P. Dal Poz ◽  
Milton H. Shimabukuro ◽  
Henrique C. de Oliveira
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Contextual Information ◽  
Three Dimensional ◽  
Extraction Methods ◽  
Aerial Images ◽  
Stereo Pair ◽  
Boundary Extraction ◽  
Building Roof ◽  
Object Space

The accelerated urban development over the last decades has made it necessary to update spatial information rapidly and constantly. Therefore, cities’ three-dimensional models have been widely used as a study base for various urban problems. However, although many efforts have been made to develop new building extraction methods, reliable and automatic extraction is still a major challenge for the remote sensing and computer vision communities, mainly due to the complexity and variability of urban scenes. This paper presents a method to extract building roof boundaries in the object space by integrating a high-resolution aerial images stereo pair, three-dimensional roof models reconstructed from light detection and ranging (LiDAR) data, and contextual information of the scenes involved. The proposed method focuses on overcoming three types of common problems that can disturb the automatic roof extraction in the urban environment: perspective occlusions caused by high buildings, occlusions caused by vegetation covering the roof, and shadows that are adjacent to the roofs, which can be misinterpreted as roof edges. For this, an improved Snake-based mathematical model is developed considering the radiometric and geometric properties of roofs to represent the roof boundary in the image space. A new approach for calculating the corner response and a shadow compensation factor was added to the model. The created model is then adapted to represent the boundaries in the object space considering a stereo pair of aerial images. Finally, the optimal polyline, representing a selected roof boundary, is obtained by optimizing the proposed Snake-based model using a dynamic programming (DP) approach considering the contextual information of the scene. The results showed that the proposed method works properly in boundary extraction of roofs with occlusion and shadows areas, presenting completeness and correctness average values above 90%, RMSE average values below 0.5 m for E and N components, and below 1 m for H component.

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

2018 ◽  
Vol 10 (9) ◽  
pp. 1459 ◽  
Author(s):  
Ying Sun ◽  
Xinchang Zhang ◽  
Xiaoyang Zhao ◽  
Qinchuan Xin
Keyword(s):  
High Resolution ◽  
Active Contour ◽  
Active Contour Model ◽  
Remote Sensing Data ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Contour Model ◽  
Optical Images ◽  
Building Footprint ◽  
Salt And Pepper

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.

Using high-resolution LiDAR data to quantify the three-dimensional structure of vegetation in urban green space

2016 ◽  
Vol 19 (4) ◽  
pp. 1749-1765 ◽  
Author(s):  
Rhiannon J. C. Caynes ◽  
Matthew G. E. Mitchell ◽  
Dan Sabrina Wu ◽  
Kasper Johansen ◽  
Jonathan R. Rhodes
Keyword(s):  
High Resolution ◽  
Green Space ◽  
Three Dimensional ◽  
Urban Green Space ◽  
Dimensional Structure ◽  
Lidar Data ◽  
Urban Green ◽  
Three Dimensional Structure

scholarly journals Supplemental Material: Development of surface ruptures by hanging-wall extension over a thrust ramp along the Ragged Mountain fault, Katalla, Alaska, USA: Applications of high-resolution three-dimensional terrain models

2020 ◽  
Author(s):  
S.N. Heinlein ◽  
et al.
Keyword(s):  
High Resolution ◽  
Digital Elevation Model ◽  
Hanging Wall ◽  
Three Dimensional ◽  
Lidar Data ◽  
Material Development ◽  
Terrain Models ◽  
Digital Elevation ◽  
Surface Ruptures ◽  
Elevation Model

<div>Video S1: Grayscale digital elevation model generated from high-resolution lidar data illustrating surface expressions at the 1 m to tens of meters scale. Video S2: False-color digital elevation model generated from high-resolution lidar data illustrating surface expressions at the 1 m to tens of meters scale.<br></div>

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

Sensor Review ◽  
2013 ◽  
Vol 33 (2) ◽  
pp. 157-165 ◽  
Author(s):  
Hui Li ◽  
Cheng Zhong ◽  
Xiaoguang Hu ◽  
Long Xiao ◽  
Xianfeng Huang
Keyword(s):  
High Resolution ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Resolution Image ◽  
High Resolution Image ◽  
New Methodologies

scholarly journals Supplemental Material: Development of surface ruptures by hanging-wall extension over a thrust ramp along the Ragged Mountain fault, Katalla, Alaska, USA: Applications of high-resolution three-dimensional terrain models

2020 ◽  
Author(s):  
S.N. Heinlein ◽  
et al.
Keyword(s):  
High Resolution ◽  
Digital Elevation Model ◽  
Hanging Wall ◽  
Three Dimensional ◽  
Lidar Data ◽  
Material Development ◽  
Terrain Models ◽  
Digital Elevation ◽  
Surface Ruptures ◽  
Elevation Model

<div>Video S1: Grayscale digital elevation model generated from high-resolution lidar data illustrating surface expressions at the 1 m to tens of meters scale. Video S2: False-color digital elevation model generated from high-resolution lidar data illustrating surface expressions at the 1 m to tens of meters scale.<br></div>

scholarly journals BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 339-344 ◽  
Author(s):  
A. P. Dal Poz ◽  
V. J. M. Fernandes
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Image Data ◽  
Region Growing ◽  
Aerial Images ◽  
Aerial Image ◽  
Lidar Data ◽  
Building Roof ◽  
Markov Random ◽  
Bounding Boxes

In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects’ polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.

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