scholarly journals URBAN AREA CHANGE DETECTION USING TIME SERIES AERIAL IMAGES

Author(s):  
C. Altuntas

The urban area should be imaged in three-dimensional (3D) for planning, inspection and management. In addition fast urbanisation requires detection the urban area changes which have been occurred with new buildings, additional floor to current buildings and excavations. 3D surface model of urban area enables to extracting high information from them. On the other hand high density spatial data should be measured to creating 3D digital terrain surface model. The dense image matching method makes 3D measurement with high density from the images in a short time. The aim of this study is detection the urban area changes via comparison of time series point cloud data from historical stereoscopic aerial images. The changes were detected with the difference of these digital elevation models. The study area was selected from Konya city in Turkey, and it has a large number of new buildings and changes in topography. Dense point cloud data were created from historical aerial images belong to years of 1951, 1975 and 2010. Every threedimensional point cloud data were registered to global georeferenced coordinate system with ground control points created from the imaged objects such as building corner, fence, wall and etc. Then urban changes were detected with comparing the dense point cloud data by exploiting the iterative closest point (ICP) algorithm. Consequently, the urban changes were detected from point to surface distances between image based point clouds.

2014 ◽  
Vol 709 ◽  
pp. 465-468
Author(s):  
Xian Quan Han ◽  
Fei Qin ◽  
Zhen Zhang ◽  
Shang Yi Yang

This paper examines the basic flow and processing of the terrestrial 3D Laser scanning technology in the tunnel survey. The use of the method is discussed, point cloud data which have been registered, cropped can be constructed to a complete tunnel surface model. An example is given to extract the tunnel section and calculate the excavation of the tunnel. Result of the experimental application of this analysis procedure is given to illustrate the proposed technique can be flexibly used according to the need based on its 3D model. The feasibility and advantages of terrestrial 3D laser scanning technology in tunnel survey is also considered.


Author(s):  
Yaneev Golombek ◽  
Wesley E. Marshall

This study investigates the feasibility of extracting streetscape features from high-density United States Geological Survey (USGS) quality level 1 (QL1) light detection and ranging (LiDAR) and quantifying the features into three-dimensional (3D) volumetric pixel (voxel) zones. As the USGS embarks on a national LiDAR database with the goal of collecting LiDAR across the continuous U.S.A., the USGS primarily requires QL2 or QL1 as a collection standard. The authors’ previous study thoroughly investigated the limits of extracting streetscape features with QL2 data, which was primarily limited to buildings and street trees. Recent studies published by other researchers that utilize advanced digital mapping techniques for streetscape measuring acknowledge that most features outside of buildings and street trees are too small to detect. QL1 data, however, is four times denser than QL2 data. This study divides streetscapes into Thiessen proximal polygons, sets voxel parameters, classifies QL1 LiDAR point cloud data, and computes quantitative statistics where classified point cloud data intersects voxels within the streetscape polygons. It demonstrates how most other common streetscape features are detectable in a standard urban QL1 dataset. In addition to street trees and buildings, one can also legitimately extract and statistically quantify walls, fences, landscape vegetation, light posts, traffic lights, power poles, power lines, street signs, and miscellaneous street furniture. Furthermore, as these features are processed into their appropriate voxel height zones, this study introduces a new methodology for obtaining comprehensive tabular descriptive statistics describing how streetscape features are truly represented in 3D.


2015 ◽  
Vol 159 ◽  
pp. 374-380 ◽  
Author(s):  
Srikant Srinivasan ◽  
Kaustubh Kaluskar ◽  
Scott Broderick ◽  
Krishna Rajan

Author(s):  
S. D. Jawak ◽  
S. N. Panditrao ◽  
A. J. Luis

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98 % for tree feature extraction and 96 % for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup.


Author(s):  
D. Frommholz ◽  
M. Linkiewicz ◽  
H. Meissner ◽  
D. Dahlke

This paper proposes a two-stage method for the reconstruction of city buildings with discontinuities and roof overhangs from oriented nadir and oblique aerial images. To model the structures the input data is transformed into a dense point cloud, segmented and filtered with a modified marching cubes algorithm to reduce the positional noise. Assuming a monolithic building the remaining vertices are initially projected onto a 2D grid and passed to RANSAC-based regression and topology analysis to geometrically determine finite wall, ground and roof planes. If this should fail due to the presence of discontinuities the regression will be repeated on a 3D level by traversing voxels within the regularly subdivided bounding box of the building point set. For each cube a planar piece of the current surface is approximated and expanded. The resulting segments get mutually intersected yielding both topological and geometrical nodes and edges. These entities will be eliminated if their distance-based affiliation to the defining point sets is violated leaving a consistent building hull including its structural breaks. To add the roof overhangs the computed polygonal meshes are projected onto the digital surface model derived from the point cloud. Their shapes are offset equally along the edge normals with subpixel accuracy by detecting the zero-crossings of the second-order directional derivative in the gradient direction of the height bitmap and translated back into world space to become a component of the building. As soon as the reconstructed objects are finished the aerial images are further used to generate a compact texture atlas for visualization purposes. An optimized atlas bitmap is generated that allows perspectivecorrect multi-source texture mapping without prior rectification involving a partially parallel placement algorithm. Moreover, the texture atlases undergo object-based image analysis (OBIA) to detect window areas which get reintegrated into the building models. To evaluate the performance of the proposed method a proof-of-concept test on sample structures obtained from real-world data of Heligoland/Germany has been conducted. It revealed good reconstruction accuracy in comparison to the cadastral map, a speed-up in texture atlas optimization and visually attractive render results.


2019 ◽  
Vol 11 (6) ◽  
pp. 729 ◽  
Author(s):  
Shiyan Pang ◽  
Xiangyun Hu ◽  
Mi Zhang ◽  
Zhongliang Cai ◽  
Fengzhu Liu

Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.


2020 ◽  
Vol 12 (16) ◽  
pp. 2561 ◽  
Author(s):  
Miktha Farid Alkadri ◽  
Francesco De Luca ◽  
Michela Turrin ◽  
Sevil Sariyildiz

This study proposes a voxel-based design approach based on the subtractive mechanism of shading envelopes and attributes information of point cloud data in tropical climates. In particular, the proposed method evaluates a volumetric sample of new buildings based on predefined shading performance criteria. With the support of geometric and radiometric information stored in point cloud, such as position (XYZ), color (RGB), and reflection intensity (I), an integrated computational workflow between passive design strategy and 3D scanning technology is developed. It aims not only to compensate for some pertinent aspects of the current 3D site modeling, such as vegetation and surrounding buildings, but also to investigate surface characteristics of existing contexts, such as visible sun vectors and material properties. These aspects are relevant for conducting a comprehensively environmental simulation, while averting negative microclimatic impacts when locating the new building into the existing context. Ultimately, this study may support architects for taking decision-making in conceptual design stage based on the real contextual conditions.


Author(s):  
X. Jian ◽  
X. Xiao ◽  
H. Chengfang ◽  
Z. Zhizhong ◽  
W. Zhaohui ◽  
...  

Airborne LiDAR technology has proven to be the most powerful tools to obtain high-density, high-accuracy and significantly detailed surface information of terrain and surface objects within a short time, and from which the Digital Elevation Model of high quality can be extracted. Point cloud data generated from the pre-processed data should be classified by segmentation algorithms, so as to differ the terrain points from disorganized points, then followed by a procedure of interpolating the selected points to turn points into DEM data. The whole procedure takes a long time and huge computing resource due to high-density, that is concentrated on by a number of researches. Hadoop is a distributed system infrastructure developed by the Apache Foundation, which contains a highly fault-tolerant distributed file system (HDFS) with high transmission rate and a parallel programming model (Map/Reduce). Such a framework is appropriate for DEM generation algorithms to improve efficiency. Point cloud data of Dongting Lake acquired by Riegl LMS-Q680i laser scanner was utilized as the original data to generate DEM by a Hadoop-based algorithms implemented in Linux, then followed by another traditional procedure programmed by C++ as the comparative experiment. Then the algorithm’s efficiency, coding complexity, and performance-cost ratio were discussed for the comparison. The results demonstrate that the algorithm's speed depends on size of point set and density of DEM grid, and the non-Hadoop implementation can achieve a high performance when memory is big enough, but the multiple Hadoop implementation can achieve a higher performance-cost ratio, while point set is of vast quantities on the other hand.


Author(s):  
M. Awrangjeb ◽  
M. K. Islam

High density airborne point cloud data has become an important means for modelling and maintenance of a power line corridor. Since, the amount of data in a dense point cloud is huge even in a small area, an automatic detection of pylons in the corridor can be a prerequisite for efficient and effective extraction of wires in a subsequent step. However, the existing solutions mostly overlook this important requirement by processing the whole data into one go, which nonetheless will hinder their applications to large areas. This paper presents a new pylon detection technique from point cloud data. First, the input point cloud is divided into ground and nonground points. The non-ground points within a specific low height region are used to generate a pylon mask, where pylons are found stand-alone, not connected with any wires. The candidate pylons are obtained using a connected component analysis in the mask, followed by a removal of trees by comparing area, shape and symmetry properties of trees and pylons. Finally, the parallelism property of wires with the line connecting pair of candidate pylons is exploited to remove trees that have the same area and shape properties as pylons. Experimental results show that the proposed technique provides a high pylon detection rate in terms of completeness (100 %) and correctness (100 %).


Sign in / Sign up

Export Citation Format

Share Document