xBD: A Dataset for Assessing Building Damage from Satellite Imagery
By Ritwik Gupta, Bryce Goodman, N. Patel, Richard Hosfelt, Sandra Sajeev, Eric T. Heim, Jigar Doshi, Keane Lucas, Howie Choset, Matthew E. Gaston. arXiv.org, 2019
397 citations recorded by the source corpus.
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 km\textsuperscript{2} of imagery.
Subfields: Atmospheric Science, Ecology, Media Technology
View Paper: https://www.semanticscholar.org/paper/8fbf011af21921a553bd8b20cd6eb16897f07801
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
In Ecology
In Media Technology
- Clifford Broni-Bediako
- Yoshua Bengio
- Junshi Xia
- Bruno Adriano
- Alexandra Sasha Luccioni
- Konrad Paul Kording
- Naoto Yokoya
- Alexandre Lacoste
Citations in the corpus
- Applications of artificial intelligence for disaster management (2020, background): “Different databases have been established for supporting damage assessment for different structures and hazards, such as xBD for building damage assessment (Gupta et al. 2019), and HOWAS21 (Kellermann et al.”
- OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping (2022, methodology): “[18] Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston.”
- FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding (2020, background): “Although several datasets [8, 7, 30, 66] are provided for post disaster damage assessments, they have numerous issues to tackle.”
- Tackling Climate Change with Machine Learning (2019, background): “ps can inform evacuation planning, retrofitting campaigns, and delivery of relief [635, 636]. Further, this imagery can assist damage assessment, by comparing scenes immediately pre- and post-disaster [637, 638]. Social media data can contain kernels of insight – places without water, clinics without”