Datasets:
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language: []
license:
- apache-2.0
multilinguality: []
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids: []
pretty_name: >-
Wildfire image classification dataset collected using images from web
searches.
Dataset Card for OpenFire
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://pyronear.org/pyro-vision/datasets.html#openfire
- Repository: https://github.com/pyronear/pyro-vision
- Point of Contact: Pyronear https://pyronear.org/en/
Dataset Summary
OpenFire is an image classification dataset for wildfire detection, collected from web searches.
Supported Tasks and Leaderboards
image-classification: The dataset can be used to train a model for Image Classification.
Languages
English
Dataset Structure
Data Instances
A data point comprises an image URL and its binary label.
{
'image_url': 'https://cdn-s-www.ledauphine.com/images/13C08274-6BA6-4577-B3A0-1E6C1B2A573C/FB1200/photo-1338240831.jpg',
'is_wildfire': true,
}
Data Fields
image_url: the download URL of the image.is_wildfire: a boolean value specifying whether there is an ongoing wildfire on the image.
Data Splits
The data is split into training and validation sets. The training set contains 7143 images and the validation set 792 images.
Dataset Creation
Curation Rationale
The curators state that the current wildfire classification datasets typically contain close-up shots of wildfires, with limited variations of weather conditions, luminosity and backrgounds, making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping with sun flares, foggy / cloudy weather conditions and small scale.
Source Data
Initial Data Collection and Normalization
OpenFire was collected using images publicly indexed by the search engine DuckDuckGo using multiple relevant queries. The images were then manually cleaned to remove errors.
Annotations
Annotation process
Each web search query was designed to yield a single label (with wildfire or without), and additional human verification was used to remove errors.
Who are the annotators?
François-Guillaume Fernandez
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
François-Guillaume Fernandez
Licensing Information
Citation Information
@software{Pyronear_PyroVision_2019,
title={Pyrovision: wildfire early detection},
author={Pyronear contributors},
year={2019},
month={October},
publisher = {GitHub},
howpublished = {\url{https://github.com/pyronear/pyro-vision}}
}