[](https://zenodo.org/badge/latestdoi/184673734)
[](https://pypi.org/project/BRAILS/)
[](https://pypi.python.org/pypi/BRAILS/)
## What is BRAILS?
BRAILS (Building and Infrastructure 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.
## Documentation
Online documentation is available at <a href="https://nheri-simcenter.github.io/BRAILS-Documentation/index.html">https://nheri-simcenter.github.io/BRAILS-Documentation</a>.
## Quickstart
### Installation
The easiest way to install the latest version of BRAILS is using ``pip``:
```
pip install BRAILS
```
### 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 types: 1) region name, 2) list of region names, 3) a tuple containing the coordinates for two opposite vertices of a bounding box for a region (e.g., ``(vert1lon,vert1lat,vert2lon,vert2lat)``), and a 4) GeoJSON file containing building footprints or location points.
InventoryGenerator automatically detects building locations in a region by downloading footprint data for the ``location`` input. The three footprint data sources, ``fpSource``, included in BRAILS are i) OpenStreetMaps, ii) Microsoft Global Building Footprints dataset, and iii) FEMA USA Structures. The keywords for these sources are ``osm``, ``ms``, and ``usastr``, respectively.
``InventoryGenerator`` also allows inventory data to be imported from the National Structure Inventory or another user-specified file to create a baseline building inventory.
Please note that to run the ``generate`` method of ``InventoryGenerator``, you will need a Google API Key.
```python
#import InventoryGenerator:
from brails.InventoryGenerator import InventoryGenerator
# Initialize InventoryGenerator:
invGenerator = InventoryGenerator(location='Berkeley, CA',
fpSource='usastr',
baselineInv='nsi',
lengthUnit='m',
outputFile='BaselineInvBerkeleyCA.geojson')
# View a list of building attributes that can be obtained using BRAILS:
invGenerator.enabled_attributes()
# 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'],
GoogleAPIKey='ENTER-YOUR-API-KEY-HERE',
nbldgs=100,
outputFile='BldgInvBerkeleyCA.geojson')
# View generated inventory:
invGenerator.inventory
```
## Acknowledgements
This work is based on material supported by the National Science Foundation under grants CMMI 1612843 and CMMI 2131111.
## Contact
NHERI-SimCenter nheri-simcenter@berkeley.edu
## How to cite
```
@software{cetiner_2024_10448047,
author = {Barbaros Cetiner and
Charles Wang and
Frank McKenna and
Sascha Hornauer and
Jinyan Zhao and
Yunhui Guo and
Stella X. Yu and
Ertugrul Taciroglu and
Kincho H. Law},
title = {BRAILS Release v3.1.1},
month = feb,
year = 2024,
publisher = {Zenodo},
version = {v3.1.1},
doi = {10.5281/zenodo.10606032},
url = {https://doi.org/10.5281/zenodo.10606032}
}
```
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