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Urban area change visualization and analysis using high density spatial data from time series aerial images Cover

Urban area change visualization and analysis using high density spatial data from time series aerial images

Reports on Geodesy and Geoinformatics
Volume 107 (2019): Issue 1 (June 2019)
By: Cihan Altuntas  
Open Access
|May 2019

Figures & Tables

Figure 1

Visualization of the study area
Visualization of the study area

Figure 2

Workflow of the applied change detection method
Workflow of the applied change detection method

Figure 3

The examples from object-based GCPs
The examples from object-based GCPs

Figure 4

Flowchart of the point cloud registration by the ICP algorithm
Flowchart of the point cloud registration by the ICP algorithm

Figure 5

The GCP locations and error estimates on point clouds of a) 1951, b) 1975, c) 1998 and d) 2010. The Z error is represented by ellipse colour. The X, Y errors are represented by ellipses. The estimated GCP locations are shown with a dot or cross. (Red rectangle indicates the study area)
The GCP locations and error estimates on point clouds of a) 1951, b) 1975, c) 1998 and d) 2010. The Z error is represented by ellipse colour. The X, Y errors are represented by ellipses. The estimated GCP locations are shown with a dot or cross. (Red rectangle indicates the study area)

Figure 6

The cross-section from compared data of 1951 and 2010. The height differences correspond to the changes.
The cross-section from compared data of 1951 and 2010. The height differences correspond to the changes.

Figure 7

The estimated changes (legend units: meters)
The estimated changes (legend units: meters)

Figure 8

The change comparison during the historical periods
The change comparison during the historical periods

Figure 9

The changes due to demolished and new buildings from 1998 to 2010 (Unit: meter)
The changes due to demolished and new buildings from 1998 to 2010 (Unit: meter)

Figure 10

The visualization of changes in comparison of time series point clouds (B1 location: 37°53’09.15”N latitude, 32°29’08.37”E longitude) (Legend unit: meter)
The visualization of changes in comparison of time series point clouds (B1 location: 37°53’09.15”N latitude, 32°29’08.37”E longitude) (Legend unit: meter)

The change quantities for the historical periods

YearRMSE [m]Average d [m]Std. dev. ᓂ [m]
1951–19758.424.826.91
1975–19988.51-0.908.46
1998–20109.106.696.18
1951–201014.1610.609.39

The informative results of dense point cloud creation

Year1951197519982010
Image #2322
Endlap70%75%70%70%
Flying altitude [km]6.277.265.38.05
Ground res. [cm/px]73.440.336.650.2
Coverage area [km2]27.622.78.5221.7
Tie points1616 of6527 of3817 of3870 of
1756665539883994
Projections32321348776347740
Reproj. err. [px]0.9890.8670.6860.259
Max. reproj. err. [px]5.8048.0346.594861.910
Dense points #2539040885987039957535243623

The RMSE of residuals on GCP coordinates after the geo-registration [m]

DateGCP #RMSEXRMSEYRMSEZRMSEXYRMSEXYZ
195161.541.601.862.222.90
197571.091.033.671.503.96
199870.801.302.521.532.94
201060.320.521.620.611.73

The ICP convergence of compared point clouds

Date comparisonConvolution [m]Mean [cm]Std. Dev. [m]
1951–19759.2e-70.060.99
1975–19987.1e-7-0.870.96
1998–20109.3e-70.090.94
1951–20109.1e-70.120.99

The properties and recording details of the images

DateCameraFocal length [m]Flying height [m]Image scaleImage dimensionsImage area [km2]Stereo area [km2]Pixel size [micron]
1951Analogue204.1862703070818 x 18 cm46.3927.623.88
1975Analogue208.1772502000018 x 18 cm23.0122.714.96
1998Analogue30553001737723 x 23 cm15.978.5220.60
2010Digital100.508050800999420 x 14430 px34.2621.77.20
DOI: https://doi.org/10.2478/rgg-2019-0001 | Journal eISSN: 2391-8152 | Journal ISSN: 0867-3179
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Language: English
Page range: 1 - 12
Submitted on: Oct 24, 2018
Accepted on: Mar 15, 2019
Published on: May 11, 2019
Published by: Warsaw University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
photogrammetry,
aerial image,
image-based point cloud,
digital elevation model,
visualization of changes,
urban area
Related subjects:
Computer sciences,
Computer sciences, other,
Geosciences,
Geodesy,
Cartography and photogrammetry,
Geosciences, other

© 2019 Cihan Altuntas, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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