xFBD: Focused Building Damage Dataset and Analysis
- URL: http://arxiv.org/abs/2212.13876v1
- Date: Fri, 23 Dec 2022 21:01:18 GMT
- Title: xFBD: Focused Building Damage Dataset and Analysis
- Authors: Dennis Melamed, Cameron Johnson, Chen Zhao, Russell Blue, Philip
Morrone, Anthony Hoogs, Brian Clipp
- Abstract summary: We propose an auxiliary challenge to the original xView2 competition.
This new challenge involves a new dataset and metrics indicating solution performance when damage is more local and limited than in xBD.
Methods that succeed on this challenge will provide more fine-grained, precise damage information than original xView2 solutions.
- Score: 7.862669992685641
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The xView2 competition and xBD dataset spurred significant advancements in
overhead building damage detection, but the competition's pixel level scoring
can lead to reduced solution performance in areas with tight clusters of
buildings or uninformative context. We seek to advance automatic building
damage assessment for disaster relief by proposing an auxiliary challenge to
the original xView2 competition. This new challenge involves a new dataset and
metrics indicating solution performance when damage is more local and limited
than in xBD. Our challenge measures a network's ability to identify individual
buildings and their damage level without excessive reliance on the buildings'
surroundings. Methods that succeed on this challenge will provide more
fine-grained, precise damage information than original xView2 solutions. The
best-performing xView2 networks' performances dropped noticeably in our new
limited/local damage detection task. The common causes of failure observed are
that (1) building objects and their classifications are not separated well, and
(2) when they are, the classification is strongly biased by surrounding
buildings and other damage context. Thus, we release our augmented version of
the dataset with additional object-level scoring metrics
https://gitlab.kitware.com/dennis.melamed/xfbd to test independence and
separability of building objects, alongside the pixel-level performance metrics
of the original competition. We also experiment with new baseline models which
improve independence and separability of building damage predictions. Our
results indicate that building damage detection is not a fully-solved problem,
and we invite others to use and build on our dataset augmentations and metrics.
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