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xBD
2D Box
Autonomous Driving
|...
许可协议: CC-BY-NC-SA 4.0

Overview

We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD provides pre- and post-event satellite imagery across a variety of disaster events with building polygons, ordinal labels of damage level, and corresponding satellite metadata. Furthermore, the dataset contains bounding boxes and labels for environmental factors such as fire, water, and smoke. xBD is the largest building damage assessment dataset to date, containing 850,736 building annotations across 45,362 km2 of imagery.

数据概要
数据格式
image,
数据量
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文件大小
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发布方
Ritwik Gupta,Richard Hosfelt,Sandra Sajeev,Nirav Patel,Bryce Goodman,Jigar Doshi,Eric Heim,Howie Choset,Matthew Gaston,Carnegie Mellon University,Software Engineering Institute,Defense Innovation Unit,Department of Defense,CrowdAI
| 数据量 -- | 大小 --
xBD
2D Box
Autonomous Driving
许可协议: CC-BY-NC-SA 4.0

Overview

We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD provides pre- and post-event satellite imagery across a variety of disaster events with building polygons, ordinal labels of damage level, and corresponding satellite metadata. Furthermore, the dataset contains bounding boxes and labels for environmental factors such as fire, water, and smoke. xBD is the largest building damage assessment dataset to date, containing 850,736 building annotations across 45,362 km2 of imagery.

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