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 |
0 to 5 of 11712
Reviews
There are no reviews for this dataset yet.
Download (1.1 GB)
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)
0 citations
3256 views
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.