EOD: The IEEE GRSS Earth Observation Database
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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.
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.
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1 of 6 references.
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Citing papers per year: 2022: 1; 2023: 3.
4 papers.
- 2022EarthNets: Empowering AI in Earth Observationcited by 40
- 2023There Are No Data Like More Data- Datasets for Deep Learning in Earth Observationcited by 16
- 2023Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciencescited by 4
- 2023Masking Hyperspectral Imaging Data with Pretrained Modelscited by 3
- 2023Masking Hyperspectral Imaging Data with Pretrained Models6 Nov 2023
- 2023There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation30 Oct 2023
- 2023Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences17 May 2023
- 2022EarthNets: Empowering AI in Earth Observation10 Oct 2022