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PRJ-3692 | MV-HarveyNET: A labelled image dataset from Hurricane Harvey for damage assessment of residential houses based on multi-view CNN
Cite This Data:
Khajwal, A., L. Tomotaki, C. Cheng, A. Noshadravan (2022). MV-HarveyNET: A labelled image dataset from Hurricane Harvey for damage assessment of residential houses based on multi-view CNN. DesignSafe-CI. https://doi.org/10.17603/ds2-wzac-h261

Authors; ; ;
Data Type(s)Dataset
Natural Hazard Type(s)Hurricane/Tropical Storm
Date of Publication2022-10-10
Related Work
KeywordsPost-disaster damage assessment, Visual dataset, Building damage, Multi-view CNN, UAV (Unmanned Aerial Vehicle) Imagery.
DOI10.17603/ds2-wzac-h261
License
 Open Data Commons Attribution
Description:

MV-HarveyNET is a visual dataset of segmented multiple views of the residential buildings damaged due to Hurricane Harvey. This dataset consists of images captured from 5 different views of 435 residential buildings located in Aransas County of Rockport, Texas. These buildings are predominantly single-story, wooden frame residential buildings that were damaged in the 2017 Hurricane Harvey. Each of these buildings are annotated by a team of expert damage inspectors from StEER using damage states labels in accordance with the guidelines in the FEMA’s HAZUS-MH Hurricane model. The expert-assigned damage states to each of these buildings can be used as the ground-truth for model training and testing. The primary goal of compiling this data is to have a dependable training dataset for developing and calibrating image-based multi-view deep learning models for improved automated post-disaster damage assessment.

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