Datasets:
Tasks:
Image Classification
Formats:
parquet
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
remote-sensing
earth-observation
geospatial
satellite-imagery
audiovisual-aerial-scene-recognition
sentinel-2
License:
File size: 3,827 Bytes
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---
language: en
license: unknown
size_categories:
- 1K<n<10K
task_categories:
- image-classification
paperswithcode_id: advance
pretty_name: ADVANCE
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- audiovisual-aerial-scene-recognition
- sentinel-2
dataset_info:
features:
- name: image
dtype: image
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': airport
'1': beach
'2': bridge
'3': farmland
'4': forest
'5': grassland
'6': harbour
'7': lake
'8': orchard
'9': residential
'10': sparse shrub land
'11': sports land
'12': train station
splits:
- name: train
num_bytes: 6698580359.05
num_examples: 5075
download_size: 6688165513
dataset_size: 6698580359.05
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# ADVANCE
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
Audiovisual Aerial Scene Recognition Dataset (ADVANCE) is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from FreeSound and Google Earth. These images are then labeled into 13 scene categories using OpenStreetMap.
- **Paper:** https://arxiv.org/abs/2005.08449
- **Homepage:** https://akchen.github.io/ADVANCE-DATASET/
## Description
<!-- Provide a longer summary of what this dataset is. -->
The **Audiovisual Aerial Scene Recognition Dataset** is a comprehensive resource designed for audiovisual aerial scene recognition tasks. It consists of 5,075 pairs of geotagged audio recordings and high-resolution 512x512 RGB images extracted from [FreeSound](https://freesound.org/browse/geotags/?c_lat=24&c_lon=20&z=2) and [Google Earth](https://earth.google.com/web/). These images are then labeled into 13 scene categories using OpenStreetMap
The dataset serves as a valuable benchmark for research and development in audiovisual aerial scene recognition, enabling researchers to explore cross-task transfer learning techniques and geotagged data analysis.
- **Total Number of Images**: 5075
- **Bands**: 3 (RGB)
- **Image Resolution**: 10mm
- **Image size**: 512x512
- **Land Cover Classes**: 13
- **Classes**: airport, beach, bridge, farmland, forest, grassland, harbour, lake, orchard, residential, sparse shrub land, sports land, train station
- **Source**: Sentinel-2
- **Dataset features**: 5,075 pairs of geotagged audio recordings and images, three spectral bands - RGB (512x512 px), 10-second audio recordings
- **Dataset format**:, images are three-channel jpgs, audio files are in wav format
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/ADVANCE")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
ADVANCE = load_dataset("blanchon/ADVANCE")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the EuroSAT dataset in your research, please consider citing the following publication:
```bibtex
@article{hu2020crosstask,
title = {Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition},
author = {Di Hu and Xuhong Li and Lichao Mou and P. Jin and Dong Chen and L. Jing and Xiaoxiang Zhu and D. Dou},
journal = {European Conference on Computer Vision},
year = {2020},
doi = {10.1007/978-3-030-58586-0_5},
bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/7fabb1ef96d2840834cfaf384408309bafc588d5}
}
```
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