metadata

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

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

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 and Google Earth. 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").

from datasets import load_dataset
ADVANCE = load_dataset("blanchon/ADVANCE")

Citation

If you use the EuroSAT dataset in your research, please consider citing the following publication:

@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}
}