# EOD: The IEEE GRSS Earth Observation Database

arXiv: https://arxiv.org/abs/2209.12480 · HTML: https://mlsanity.com/paper/2209.12480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [There is no data like more data -- current status of machine learning datasets in remote sensing](https://mlsanity.com/paper/2105.11726/) (2021)

## Built on by (most cited first, 4 of 4)

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation](https://mlsanity.com/paper/2310.19231/) (2023)
- [Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences](https://mlsanity.com/paper/2305.09928/) (2023)
- [Masking Hyperspectral Imaging Data with Pretrained Models](https://mlsanity.com/paper/2311.03053/) (2023)

## Abstract (arXiv)

In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community. In the last decade, a plethora of different datasets was published, each designed for a specific data type and with a specific task or application in mind. In the jungle of remote sensing datasets, it can be hard to keep track of what is available already. With this paper, we introduce EOD - the IEEE GRSS Earth Observation Database (EOD) - an interactive online platform for cataloguing different types of datasets leveraging remote sensing imagery.