🤗 Add DatasetCard

README.md

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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
+---
+
+# 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}
+}
+```