Multimodal Damage Identification for Humanitarian Computing
Donated on 5/31/2018
5879 captioned images (image and text) from social media related to damage during natural disasters/wars, and belong to 6 classes: Fires, Floods, Natural landscape, Infrastructural, Human, Non-damage.
Dataset Characteristics
Multivariate, Text
Subject Area
Social Science
Associated Tasks
Classification
Feature Type
Integer
# Instances
5879
# Features
-
Dataset Information
Additional Information
Samples were retrieved from social media posts including Instagram and Twitter.
Has Missing Values?
No
Variable Information
640x640 RGB images and raw text
Dataset Files
| File | Size |
|---|---|
| multimodal/damaged_nature/images/naturaldisaster_2017-10-15_16-18-56.jpg | 2.7 MB |
| multimodal/non_damage/images/nature_2017-10-30_17-46-33.jpg | 2.4 MB |
| multimodal/flood/images/floodwater_2017-08-27_18-37-35.jpg | 2.3 MB |
| multimodal/damaged_nature/images/naturaldisaster_2017-10-22_01-32-11.jpg | 2.3 MB |
| multimodal/non_damage/images/ad_2017-11-25_01-49-46.jpg | 2.2 MB |
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Install the ucimlrepo package
pip install ucimlrepo
Import the dataset into your code
View the full documentationfrom ucimlrepo import fetch_ucirepo # fetch dataset multimodal_damage_identification_for_humanitarian_computing = fetch_ucirepo(id=456) # data (as pandas dataframes) X = multimodal_damage_identification_for_humanitarian_computing.data.features y = multimodal_damage_identification_for_humanitarian_computing.data.targets # metadata print(multimodal_damage_identification_for_humanitarian_computing.metadata) # variable information print(multimodal_damage_identification_for_humanitarian_computing.variables)
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Citation
Mouzannar, H., Rizk, Y., & Awad, M. (2018). Multimodal Damage Identification for Humanitarian Computing [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C52P6P.
Style:
Creators
Hussein Mouzannar
Yara Rizk
Mariette Awad
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.