Automatic Road Network Recognition and Extraction for Urban Planning
The uses of road map in daily activities are numerous
but it is a hassle to construct and update a road map whenever there
are changes. In Universiti Malaysia Sarawak, research on Automatic
Road Extraction (ARE) was explored to solve the difficulties in
updating road map. The research started with using Satellite Image
(SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space
Segmentation & Edge Detection (Hybrid SCSS-EDGE) algorithm
was developed to extract roads automatically from satellite-taken
images. In order to extract the road network accurately, the satellite
image must be analyzed prior to the extraction process. The
characteristics of these elements are analyzed and consequently the
relationships among them are determined. In this study, the road
regions are extracted based on colour space elements and edge details
of roads. Besides, edge detection method is applied to further filter
out the non-road regions. The extracted road regions are validated by
using a segmentation method. These results are valuable for building
road map and detecting the changes of the existing road database.
The proposed Hybrid Simple Colour Space Segmentation and Edge
Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks
fully automatic, where the user only needs to input a high-resolution
satellite image and wait for the result. Moreover, this system can
work on complex road network and generate the extraction result in
seconds.
[1] S. Hinz, A. Baumgartner, H. Ebner. (2001). "Modelling Contextual
Knowledge for Controlling Road Extraction in Urban Areas."
IEEE/ISPRS Joint Workshop on Remote Sensing and Dara Fusion over
Urban Areas, Pp. 40-44.
[2] X.Y. Jin, and C.H. Davis. (2003). "Multispectral IKONOS Imagery
Automatic Road Extraction from High-Resolution." Proceedings of
IEEE International Geoscience and Remote Sensing Symposium, Vol 3;
Pp.1730-1732.
[3] A.Chang S.K. Kyu, B.R. Sang. (1997). "A Road Extraction Method
from Topographical Map Images." IEEE Communications, Computers
and Signal Processing, Vol. 2; Pp. 839-842.
[4] C.L. Jia, K.F. Ji, Y.M. Jiang, G.Y. Kuang. (2005). "Road Extraction
from High-Resolution SAR Imagery Using Hough Transform."
Proceedings of IEEE International Geoscience and Remote Sensing
Symposium, Vol. 1; Pp. 1-4.
[5] W.R. Chen, C. Wang, H. Zhang. (2004). "Road Network Extraction in
High Resolution SAR Images." Proceedings of IEEE International
Geoscience and Remote Sensing Symposium, Vol. 6; Pp. 3806 - 3809.
[6] P. Gamba, F. Dell-Acqua, G. Lisini. (2006). "Improving Urban Road
Extraction in High-Resolution Image Exploiting Directional Filtering,
Perceptual Grouping, and Simple Topological Concepts." IEEE
Geoscience and Remote Sensing Letters, Vol. 3, No. 3; Pp. 387-391.
[7] L. Wang, Q. Qin, S. Du, D. Chen, J. Tao. (2006). "Road Extraction from
Remote Sensing Image Based on Multi-resolution Analysis."
International Symposium on Remote Sensing of Environment (ISRSE).
[8] G. Lisini, C. Tison, F. Tupin, P. Gamba. (2006). "Feature Fusion to
Improve Road Network Extraction in High-Resolution SAR Images."
IEEE Geoscience and Remote Sensing Letters, Vol. 3, No. 2; Pp. 217-
221.
[9] X, Li, Y. Qiao, W. Yi, Z. Guo. (2003). "The Research of Road
Extraction for High Resolution Satellite Image." IEEE Geoscience and
Remote Sensing Symposium, Vol. 6; Pp. 3949-3951.
[10] M. Barzohar, D.B. Cooper. (1996). "Automatic finding of main roads in
aerial images by using geometric-stochastic models and estimation."
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.
18, No.7; Pp. 707-721.
[11] Y.G. Xiao, T.S. Tan, S.C. Tay. (2005). "Utilizing Edge to Extract Roads
in High-Resolution Satellite Imagery". IEEE International Conference
on Image Processing, Vol. 1; Pp. 637-640.
[1] S. Hinz, A. Baumgartner, H. Ebner. (2001). "Modelling Contextual
Knowledge for Controlling Road Extraction in Urban Areas."
IEEE/ISPRS Joint Workshop on Remote Sensing and Dara Fusion over
Urban Areas, Pp. 40-44.
[2] X.Y. Jin, and C.H. Davis. (2003). "Multispectral IKONOS Imagery
Automatic Road Extraction from High-Resolution." Proceedings of
IEEE International Geoscience and Remote Sensing Symposium, Vol 3;
Pp.1730-1732.
[3] A.Chang S.K. Kyu, B.R. Sang. (1997). "A Road Extraction Method
from Topographical Map Images." IEEE Communications, Computers
and Signal Processing, Vol. 2; Pp. 839-842.
[4] C.L. Jia, K.F. Ji, Y.M. Jiang, G.Y. Kuang. (2005). "Road Extraction
from High-Resolution SAR Imagery Using Hough Transform."
Proceedings of IEEE International Geoscience and Remote Sensing
Symposium, Vol. 1; Pp. 1-4.
[5] W.R. Chen, C. Wang, H. Zhang. (2004). "Road Network Extraction in
High Resolution SAR Images." Proceedings of IEEE International
Geoscience and Remote Sensing Symposium, Vol. 6; Pp. 3806 - 3809.
[6] P. Gamba, F. Dell-Acqua, G. Lisini. (2006). "Improving Urban Road
Extraction in High-Resolution Image Exploiting Directional Filtering,
Perceptual Grouping, and Simple Topological Concepts." IEEE
Geoscience and Remote Sensing Letters, Vol. 3, No. 3; Pp. 387-391.
[7] L. Wang, Q. Qin, S. Du, D. Chen, J. Tao. (2006). "Road Extraction from
Remote Sensing Image Based on Multi-resolution Analysis."
International Symposium on Remote Sensing of Environment (ISRSE).
[8] G. Lisini, C. Tison, F. Tupin, P. Gamba. (2006). "Feature Fusion to
Improve Road Network Extraction in High-Resolution SAR Images."
IEEE Geoscience and Remote Sensing Letters, Vol. 3, No. 2; Pp. 217-
221.
[9] X, Li, Y. Qiao, W. Yi, Z. Guo. (2003). "The Research of Road
Extraction for High Resolution Satellite Image." IEEE Geoscience and
Remote Sensing Symposium, Vol. 6; Pp. 3949-3951.
[10] M. Barzohar, D.B. Cooper. (1996). "Automatic finding of main roads in
aerial images by using geometric-stochastic models and estimation."
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.
18, No.7; Pp. 707-721.
[11] Y.G. Xiao, T.S. Tan, S.C. Tay. (2005). "Utilizing Edge to Extract Roads
in High-Resolution Satellite Imagery". IEEE International Conference
on Image Processing, Vol. 1; Pp. 637-640.
@article{"International Journal of Architectural, Civil and Construction Sciences:52880", author = "D. B. L. Bong and K.C. Lai and A. Joseph", title = "Automatic Road Network Recognition and Extraction for Urban Planning", abstract = "The uses of road map in daily activities are numerous
but it is a hassle to construct and update a road map whenever there
are changes. In Universiti Malaysia Sarawak, research on Automatic
Road Extraction (ARE) was explored to solve the difficulties in
updating road map. The research started with using Satellite Image
(SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space
Segmentation & Edge Detection (Hybrid SCSS-EDGE) algorithm
was developed to extract roads automatically from satellite-taken
images. In order to extract the road network accurately, the satellite
image must be analyzed prior to the extraction process. The
characteristics of these elements are analyzed and consequently the
relationships among them are determined. In this study, the road
regions are extracted based on colour space elements and edge details
of roads. Besides, edge detection method is applied to further filter
out the non-road regions. The extracted road regions are validated by
using a segmentation method. These results are valuable for building
road map and detecting the changes of the existing road database.
The proposed Hybrid Simple Colour Space Segmentation and Edge
Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks
fully automatic, where the user only needs to input a high-resolution
satellite image and wait for the result. Moreover, this system can
work on complex road network and generate the extraction result in
seconds.", keywords = "Road Network Recognition, Colour Space, Edge
Detection, Urban Planning.", volume = "3", number = "5", pages = "211-7", }