Releases · NHERI-SimCenter/BRAILS

Enabled rapidtools feature to extract building-level imagery from NHERI RAPID aerial and street-level image data
Added the capability to pull National Structure Inventory as baseline building inventory
Updated TranspInventoryGenerator for more stable API queries
Updated FacadeParser for more effective memory utilization

Assets 2

01 Jan 03:04

fmckenna

v3.1.0

b349f54

Version 3.1.0

Major Changes:

Enabled Transportation inventory generation for roadways, bridges, tunnels, and railroads
Revised FacadeParser that calculates dimensions from depth map as opposed to camera coordinates for more robust dimension predictions
Added FEMA USA Structures as an additional building footprint source

Assets 2

30 Sep 23:10

bacetiner

v3.0.0

6848269

Release v3.0.0

BRAILS version 3.0.0 contains a new interface that simplifies inventory generation and training image classification/semantic segmentation models.

In terms of inventory generation capabilities, BRAILS now contains modules that can predict: 1) roof type, 2) roof cover type, 3) occupancy class, 4) era of construction, 5) the number of floors, 6) building height, 7) roof eave height, 8) roof pitch, 9) facade window area, 10) first-floor height, 11) existence of chimneys, and 12) existence of garages.

The new InventoryGenerator workflow offers a convenient end-to-end workflow to create building inventories. InventoryGenerator

Extracts location polynomials using Nominatim API,
Parses footprint information from Microsoft Footprint Database or OpenStreetMaps,
Downloads Google street-level or satellite imagery explicitly focused on each building in the region specified by the user,
Passes these images through BRAILS modules, and
Prints out simple CSV files for ingestion into many other platforms, such as R2D or QGIS.

All of this can now be performed in a few lines of code.

BRAILS now also offers automated pipelines for training image classification and semantic segmentation models. Using ImageClassifier and ImageSegmenter, it is possible to train state-of-the-art deep learning models without needing to spend developing time on the training step of the process.

Assets 2

01 Mar 16:37

charlesxwang

v2.0.0

4378732

Release v2.0.0

BRAILS version 2.0 is re-structured with modules for performing specific analyses of images. The expanded module library enables BRAILS’ capability of predicting a larger spectrum of building attributes including occupancy type, roof type, foundation elevation, year built, soft-story, number of floors.

The new version also features a streamlined workflow, CityBuilder, for automatic creation of regional-scale building inventories by fusing multiple sources of data such as OpenStreetMap, Microsoft Footprint Data, and Google Maps, and extracting information from them using the modules.

Assets 3