Abstract
3D city modeling is a new development trend in cartography that has a lot of practical and scientific value. The project necessitates the extraction of a building footprint using remote sensing images. This research examined how to solve the Building Footprint problem using automatic segmentation methods. We reviewed popular segmentation models as Mask-RCNN, U-net, and U2-net, and developed two multi-models that generated more stable and good results than the single models.
T. A. Tuan and H. P. Long—These authors contributed equally to this work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Emek, R.A., Demir, N.: Building detection from SAR images using UNET deep learning method, pp. 215–218 (2020). https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-215-2020
We, X., et al.: Building outline extraction directly using the u2-net semantic segmentation model from high-resolution aerial images and a comparison study. Remote. Sens. 13, 3187 (2021)
Zhao, K., Kang, J., Jung, J., Sohn, G.: Building extraction from satellite images using mask R-CNN with building boundary regularization. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 242–2424 (2018). https://doi.org/10.1109/CVPRW.2018.00045
Qinzhe, H., Yin, Q., Zheng, X., Chen, Z.: Remote sensing image building detection method based on mask r-cnn. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00322-z
USGS: Sunnyvale uav images. https://earthexplorer.usgs.gov/
OSM: Sunnyvale uav labels. https://www.openstreetmap.org/
Kaggle: 2018 data science bowl (2018). https://www.kaggle.com/c/data-science-bowl-2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Anh, H.T., Tuan, T.A., Long, H.P., Ha, L.H., Thang, T.N. (2022). Multi Deep Learning Model for Building Footprint Extraction from High Resolution Remote Sensing Image. In: Anh, N.L., Koh, SJ., Nguyen, T.D.L., Lloret, J., Nguyen, T.T. (eds) Intelligent Systems and Networks. Lecture Notes in Networks and Systems, vol 471. Springer, Singapore. https://doi.org/10.1007/978-981-19-3394-3_29
Download citation
DOI: https://doi.org/10.1007/978-981-19-3394-3_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-3393-6
Online ISBN: 978-981-19-3394-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)