BRAILS 1.9.6

Building Recognition using AI at Large-Scale

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 pipeline for city-scale building information modeling (BIM).

How to install

pip install BRAILS

How to use

Example 1

The following example shows how to use BRAILS modules.

The example can be found here, or you can test it in this notebook on Google Colab.

Example images can be downloaded like this.

wget https://zenodo.org/record/4095668/files/image_examples.zip

# import modules
from brails.modules import RoofClassifier, OccupancyClassifier, SoftstoryClassifier


# initialize a roof classifier
roofModel = RoofClassifier()

# initialize an occupancy classifier
occupancyModel = OccupancyClassifier()

# initialize a soft-story classifier
ssModel = SoftstoryClassifier()

# use the roof classifier 

imgs = ['image_examples/Roof/gabled/76.png',
        'image_examples/Roof/hipped/54.png',
        'image_examples/Roof/flat/94.png']

predictions = roofModel.predict(imgs)

# use the occupancy classifier 

imgs = ['image_examples/Occupancy/RES1/51563.png',
        'image_examples/Occupancy/RES3/65883.png']

predictions = occupancyModel.predict(imgs)

# use the softstory classifier 

imgs = ['image_examples/Softstory/Others/3110.jpg',
        'image_examples/Softstory/Softstory/901.jpg']

predictions = ssModel.predict(imgs)

The predictions look like this:

Image :  image_examples/Roof/gabled/76.png     Class : gabled (83.21%)
Image :  image_examples/Roof/hipped/54.png     Class : hipped (100.0%)
Image :  image_examples/Roof/flat/94.png     Class : flat (97.68%)
Results written in file roofType_preds.csv

Image :  image_examples/Occupancy/RES1/51563.png     Class : RES1 (66.41%)
Image :  image_examples/Occupancy/RES3/65883.png     Class : RES3 (49.51%)
Results written in file occupancy_preds.csv

Image :  image_examples/Softstory/Others/3110.jpg     Class : others (96.13%)
Image :  image_examples/Softstory/Softstory/901.jpg     Class : softstory (96.31%)
Results written in file softstory_preds.csv

Example 2

This example shows how to create a building inventory by specifying the name of a city.

You can also specify a bounding box.

Check details of this example and more examples here, or test them in this notebook on Google Colab.

# Import the module from BRAILS
from brails.CityBuilder import CityBuilder

# Initialize the CityBuilder
cityBuilder = CityBuilder(attributes=['softstory','occupancy','roofshape'], 
                   numBldg=10,random=True, place='Lake Charles, Louisiana', 
                   GoogleMapAPIKey='put-your-key-here')

# create the city-scale BIM file
BIM = cityBuilder.build()

The definitions of the parameters in this example can be found here.

The result BIM is a geopandas dataframe:

index geometry	                                       ID	roofShape	roofShapeProb	softStory	softStoryProb	occupancy	occupancyProb
0	POLYGON ((-93.21912 30.22786, -93.21892 30.227...	0	softstory	0.761644	    COM	        0.878260	    flat	    0.999769
1	POLYGON ((-93.21517 30.22412, -93.21491 30.224...	1	softstory	0.500260	    RES1	    0.549517	    hipped	    0.961910
2	POLYGON ((-93.21411 30.22617, -93.21427 30.226...	2	softstory	0.994021	    COM	        0.594705	    hipped	    0.999313
3	POLYGON ((-93.16719 30.23006, -93.16715 30.230...	3	softstory	0.969902	    COM	        0.372285	    flat	    0.735594
4	POLYGON ((-93.25565 30.21074, -93.25550 30.211...	4	softstory	0.000000	    COM	        0.000000	    flat	    0.998508
5	POLYGON ((-93.20388 30.22758, -93.20388 30.227...	5	others	    0.971890	    COM	        0.913591	    flat	    0.996405
6	POLYGON ((-93.21610 30.22505, -93.21613 30.224...	6	softstory	0.000000	    COM	        0.000000	    flat	    0.963075
7	POLYGON ((-93.16549 30.22877, -93.16546 30.228...	7	others	    0.841312	    RES3	    0.441689	    hipped	    0.534562
8	POLYGON ((-93.21525 30.22513, -93.21523 30.225...	8	others	    0.830462	    RES1	    0.383844	    flat	    0.786514
9	POLYGON ((-93.21924 30.23054, -93.21949 30.230...	9	softstory	0.000000	    COM	        0.000000	    flat	    0.986143

Documents

Read the online document here.

How to cite

@article{wang2021machine,
  title={Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management},
  author={Wang, Chaofeng and Yu, Qian and Law, Kincho H and McKenna, Frank and Stella, X Yu and Taciroglu, Ertugrul and Zsarn{\'o}czay, Adam and Elhaddad, Wael and Cetiner, Barbaros},
  journal={Automation in Construction},
  volume={122},
  pages={103474},
  year={2021},
  publisher={Elsevier},
  doi="https://doi.org/10.1016/j.autcon.2020.103474"
}

The pdf is here.

Acknowledgement

This material is based upon work supported by the National Science Foundation under Grant No. 1612843.

Contact

Charles Wang, NHERI SimCenter, UC Berkeley, [e-mail]