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Building Recognition Using AI at Large-Scale

Project description

What is BRAILS?

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



The easiest way to install the latest version of BRAILS is using pip:

pip install git+

Example: InventoryGenerator Workflow

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,

# Run InventoryGenerator to generate an inventory for the entered location:
# To run InventoryGenerator for all enabled attributes set attributes='all':

# View generated inventory:


This work is based on material supported by the National Science Foundation under grants CMMI 1612843 and CMMI 2131111.



How to cite

  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          = {}

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