publication . Preprint . Conference object . 2019

Road Network Reconstruction from satellite images with Machine Learning Supported by Topological Methods

Tamal K. Dey; Jiayuan Wang; Yusu Wang;
Open Access English
  • Published: 15 Sep 2019
Abstract
Comment: 26 pages, 13 figures, ACM SIGSPATIAL 2019
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Funded by
NSF| RI: Small: Learning discrete structure from continuous spaces
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1815697
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
,
NSF| AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1733798
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Computing and Communication Foundations
,
NSF| TRIPODS: Topology, Geometry, and Data Analysis (TGDA@OSU):Discovering Structure, Shape, and Dynamics in Data
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1740761
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Computing and Communication Foundations
23 references, page 1 of 2

[3] A. Buslaev, S. Seferbekov, V. Iglovikov, and A. Shvets. Fully convolutional network for automatic road extraction from satellite imagery. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018. [OpenAIRE]

[4] D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in neural information processing systems, pages 2843{2851, 2012. [OpenAIRE]

[5] S. Das, TT Mirnalinee, and K. Varghese. Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images. IEEE transactions on Geoscience and Remote sensing, 49(10):3906{3931, 2011. [OpenAIRE]

[6] O. Delgado-Friedrichs, V. Robins, and A. Sheppard. Skeletonization and partitioning of digital images using discrete morse theory. IEEE Trans. Pattern Anal. Machine Intelligence, 37(3):654{666, March 2015.

[7] T. K. Dey, J. Wang, and Y. Wang. Improved road network reconstruction using discrete morse theory. In Proc. 25th ACM SIGSPATIAL, page 58. ACM, 2017.

[8] T. K. Dey, J. Wang, and Y. Wang. Graph Reconstruction by Discrete Morse Theory. In Proc. 34th Sympos. Comput. Geom., pages 31:1{31:15, 2018.

[9] H. Edelsbrunner and J. Harer. Computational Topology : an Introduction. American Mathematical Society, 2010.

[10] H. Edelsbrunner, D. Letscher, and A. Zomorodian. Topological persistence and simpli cation. Discr. Comput. Geom., 28:511{533, 2002. [OpenAIRE]

[11] R. Forman. Morse theory for cell complexes. Advances in mathematics, 134(1):90{145, 1998.

[12] X. Gu, A. Zang, X. Huang, A. Tokuta, and X. Chen. Fusion of color images and lidar data for lane classi cation. In Proc. 23rd ACM SIGSPATIAL, page 69. ACM, 2015.

[13] A. Gyulassy, M. Duchaineau, V. Natarajan, V. Pascucci, E. Bringa, A. Higginbotham, and B. Hamann. Topologically clean distance elds. IEEE Trans. Visualization Computer Graphics, 13(6):1432{1439, Nov 2007. [OpenAIRE]

[14] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proc. of the IEEE conference on computer vision and pattern recognition, pages 770{778, 2016.

[15] V. Robins, P. J. Wood, and A. P. Sheppard. Theory and algorithms for constructing discrete morse complexes from grayscale digital images. IEEE Trans. Pattern Anal. Machine Intelligence, 33(8):1646{1658, Aug 2011.

[16] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234{ 241. Springer, 2015.

[17] W. Shi, Z. Miao, and J. Debayle. An integrated method for urban main-road centerline extraction from optical remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(6):3359{3372, 2014.

23 references, page 1 of 2
Abstract
Comment: 26 pages, 13 figures, ACM SIGSPATIAL 2019
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition
Funded by
NSF| RI: Small: Learning discrete structure from continuous spaces
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1815697
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
,
NSF| AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1733798
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Computing and Communication Foundations
,
NSF| TRIPODS: Topology, Geometry, and Data Analysis (TGDA@OSU):Discovering Structure, Shape, and Dynamics in Data
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1740761
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Computing and Communication Foundations
23 references, page 1 of 2

[3] A. Buslaev, S. Seferbekov, V. Iglovikov, and A. Shvets. Fully convolutional network for automatic road extraction from satellite imagery. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018. [OpenAIRE]

[4] D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in neural information processing systems, pages 2843{2851, 2012. [OpenAIRE]

[5] S. Das, TT Mirnalinee, and K. Varghese. Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images. IEEE transactions on Geoscience and Remote sensing, 49(10):3906{3931, 2011. [OpenAIRE]

[6] O. Delgado-Friedrichs, V. Robins, and A. Sheppard. Skeletonization and partitioning of digital images using discrete morse theory. IEEE Trans. Pattern Anal. Machine Intelligence, 37(3):654{666, March 2015.

[7] T. K. Dey, J. Wang, and Y. Wang. Improved road network reconstruction using discrete morse theory. In Proc. 25th ACM SIGSPATIAL, page 58. ACM, 2017.

[8] T. K. Dey, J. Wang, and Y. Wang. Graph Reconstruction by Discrete Morse Theory. In Proc. 34th Sympos. Comput. Geom., pages 31:1{31:15, 2018.

[9] H. Edelsbrunner and J. Harer. Computational Topology : an Introduction. American Mathematical Society, 2010.

[10] H. Edelsbrunner, D. Letscher, and A. Zomorodian. Topological persistence and simpli cation. Discr. Comput. Geom., 28:511{533, 2002. [OpenAIRE]

[11] R. Forman. Morse theory for cell complexes. Advances in mathematics, 134(1):90{145, 1998.

[12] X. Gu, A. Zang, X. Huang, A. Tokuta, and X. Chen. Fusion of color images and lidar data for lane classi cation. In Proc. 23rd ACM SIGSPATIAL, page 69. ACM, 2015.

[13] A. Gyulassy, M. Duchaineau, V. Natarajan, V. Pascucci, E. Bringa, A. Higginbotham, and B. Hamann. Topologically clean distance elds. IEEE Trans. Visualization Computer Graphics, 13(6):1432{1439, Nov 2007. [OpenAIRE]

[14] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proc. of the IEEE conference on computer vision and pattern recognition, pages 770{778, 2016.

[15] V. Robins, P. J. Wood, and A. P. Sheppard. Theory and algorithms for constructing discrete morse complexes from grayscale digital images. IEEE Trans. Pattern Anal. Machine Intelligence, 33(8):1646{1658, Aug 2011.

[16] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234{ 241. Springer, 2015.

[17] W. Shi, Z. Miao, and J. Debayle. An integrated method for urban main-road centerline extraction from optical remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(6):3359{3372, 2014.

23 references, page 1 of 2
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