BRAILS (Building Recognition using AI at Large-Scale) provides a set of Python modules that utilize deep learning (DL), and computer vision (CV) techniques to extract information from satellite and street level images. The BRAILS framework also provides turn-key applications allowing users to put individual modules together to determine multiple attributes in a single pass or train general-purpose image classification, object detection, or semantic segmentation models.
Online documentation is available at https://nheri-simcenter.github.io/BRAILS-Documentation.
The easiest way to install the latest version of BRAILS is using pip:
pip install BRAILS
This example demonstrates how to use the InventoryGenerator method embedded in BRAILS to generate regional-level inventories.
The primary input to InventoryGenerator is location. InventoryGenerator accepts four different location input: 1) region name, 2) list of region names, 3) bounding box of a region, 4) A GeoJSON file containing building footprints.
Please note that you will need a Google API Key to run InventoryGenerator.
#import InventoryGenerator:
from brails.InventoryGenerator import InventoryGenerator
# Initialize InventoryGenerator:
invGenerator = InventoryGenerator(location='Berkeley, CA',
nbldgs=100, randomSelection=True,
GoogleAPIKey="")
# Run InventoryGenerator to generate an inventory for the entered location:
# To run InventoryGenerator for all enabled attributes set attributes='all':
invGenerator.generate(attributes=['numstories','roofshape','buildingheight'])
# View generated inventory:
invGenerator.inventoryThis work is based on material supported by the National Science Foundation under grants CMMI 1612843 and CMMI 2131111.
NHERI-SimCenter nheri-simcenter@berkeley.edu
@software{cetiner_2022_7132010,
author = {Barbaros Cetiner and
Charles Wang and
Frank McKenna and
Sascha Hornauer and
Yunhui Guo},
title = {BRAILS Release v3.0.0},
month = sep,
year = 2022,
note = {{This work is based on material supported by the
National Science Foundation under grants CMMI
1612843 and CMMI 2131111}},
publisher = {Zenodo},
version = {v3.0.0},
doi = {10.5281/zenodo.7132010},
url = {https://doi.org/10.5281/zenodo.7132010}
}