Building Recognition using AI at Large-Scale

What is BRAILS

BRAILS is the acronym for Building Recognition using AI at Large-Scale, which is an AI-Based software for city information modeling.

BRAILS uses deep learning technologies to extract information from satellite or street view images, providing the architecture, engineering, and construction (AEC) professionals the insight and tools to more efficiently plan, design, construct, and manage buildings and infrastructure systems.

User Manual

• 1. Acknowledgments
• 2. About
• 3. Installation
• 4. User Guide
• 5. Troubleshooting
• 6. Examples
• 7. Bugs & Feauture Requests
• 8. Copyright and License

Technical Manual

• 1. Theory and Implementation
• 2. Verification and Validation

Developer Manual

• 1. How to Extend
• 2. Coding Style

How to cite

Charles Wang, Qian Yu, Frank McKenna, Barbaros Cetiner, Stella X. Yu, Ertugrul Taciroglu & Kincho H. Law. (2019, October 11). NHERI-SimCenter/BRAILS: v1.0.1 (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.3483208

Contact

Charles Wang, NHERI SimCenter, University of California, Berkeley, c_w@berkeley.edu

References

[BHF+19]

Benjamin Bischke, Patrick Helber, Joachim Folz, Damian Borth, and Andreas Dengel. Multi-task learning for segmentation of building footprints with deep neural networks. 2019 IEEE International Conference on Image Processing (ICIP), pages 1480–1484, 2019.

[Goo97]

Pierre Goovaerts. Geostatistics for natural resources evaluation. Oxford University Press on Demand, 1997.

[HW68]

David H Hubel and Torsten N Wiesel. Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology, 195(1):215–243, 1968.

[LHF+19]

Weijia Li, Conghui He, Jiarui Fang, Juepeng Zheng, Haohuan Fu, and Le Yu. Semantic segmentation-based building footprint extraction using very high-resolution satellite images and multi-source gis data. Remote Sensing, 11(4):403, 2019.

[Mic]

Microsoft. US Building Footprints. URL: https://github.com/microsoft/USBuildingFootprints.

[SVI+16]

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826, 2016.

[Van10]

Erik Vanmarcke. Random fields: analysis and synthesis. World Scientific, 2010.

[WC18]

C Wang and Q Chen. A hybrid geotechnical and geological data-based framework for multiscale regional liquefaction hazard mapping. Géotechnique, 68(7):614–625, 2018.

[Wan20]

Chaofeng Wang. Occupancy test. December 2020. URL: https://doi.org/10.5281/zenodo.4386991, doi:10.5281/zenodo.4386991.

[WCSJ17]

Chaofeng Wang, Qiushi Chen, Mengfen Shen, and C Hsein Juang. On the spatial variability of cpt-based geotechnical parameters for regional liquefaction evaluation. Soil Dynamics and Earthquake Engineering, 95:153–166, 2017.

[Wan19]

Charles Wang. Random satellite images of buildings. October 2019. URL: https://doi.org/10.5281/zenodo.3521067, doi:10.5281/zenodo.3521067.

[WYM+19]

Charles Wang, Qian Yu, Frank McKenna, Barbaros Cetiner, Stella X. Yu, Ertugrul Taciroglu, and Kincho H. Law. Nheri-simcenter/brails: v1.0.1. October 2019. URL: https://doi.org/10.5281/zenodo.3483208, doi:10.5281/zenodo.3483208.

[ZKJS18]

Kang Zhao, Jungwon Kang, Jaewook Jung, and Gunho Sohn. Building extraction from satellite images using mask r-cnn with building boundary regularization. CVPR Workshops, pages 247–251, 2018.