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}
}