README.md

---
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 -->
![ADVANCE](./thumbnail.png)

<!-- 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}
}
```