task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
- Volcanoes
- Plumes
- UAVs
- Drone
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': plume
'1': summit
splits:
- name: train
num_bytes: 29846342.127
num_examples: 1211
- name: validation
num_bytes: 7311174
num_examples: 294
- name: test
num_bytes: 12048406
num_examples: 456
download_size: 49324639
dataset_size: 49205922.127000004
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
license: mit
language:
- en
Dataset Description
The dataset presents labelled pictures of plumes and of the Fuego Summit in Guatemala. The data was collected by the University of Bristol Flight Lab in Guatemala from March 22 to April 3, 2019. The drone used for this purpose was a Skywalker X8, equipped with a Pixhawk onboard computer running ArduPlane 3.7.1 and a Raspberry Pi 3B+ for mission management and communication with the ground station. The drone was also equipped of a GoPro Hero 9.
Citation
@misc{
}
Acknowledgement
This work is supported by the WildDrone MSCA Doctoral Network funded by EU Horizon Europe under grant agreement no. 101071224, the Innovation Fund Denmark for the project DIREC (9142-00001B), and by the Engineering & Physical Sciences Research Council (UK) through the CASCADE (Complex Autonomous aircraft Systems Configuration, Analysis and Design Exploratory) programme grant (EP/R009953/1).
Dataset Labels
['plume', 'summit']
Example of Labelled Images
Number of Images
{'valid': 294, 'test': 456, 'train': 1211}
Example of Application
The dataset was used to train a YOLOv8 neural network. More details can be found in the paper mentioned in the citation section. The following video presents the model output for an entire flight.
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
from datasets import load_dataset
ds = load_dataset("edouard-rolland/volcanic-plumes", name="full")
example = ds['train'][0]
License
MIT