# There is no data like more data -- current status of machine learning datasets in remote sensing

arXiv: https://arxiv.org/abs/2105.11726 · HTML: https://mlsanity.com/paper/2105.11726/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 25 May 2021.

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

- [Functional Map of the World](https://mlsanity.com/paper/1711.07846/) (2017)

## Built on by (most cited first, 6 of 6)

- [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)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [EOD: The IEEE GRSS Earth Observation Database](https://mlsanity.com/paper/2209.12480/) (2022)
- [MapInWild: A Remote Sensing Dataset to Address the Question What Makes Nature Wild](https://mlsanity.com/paper/2212.02265/) (2022)
- [Benchmarking for Practice: Few-Shot Time-Series Crop-Type Classification on the EuroCropsML Dataset](https://mlsanity.com/paper/2504.11022/) (2025)

## Abstract (arXiv)

Annotated datasets have become one of the most crucial preconditions for the development and evaluation of machine learning-based methods designed for the automated interpretation of remote sensing data. In this paper, we review the historic development of such datasets, discuss their features based on a few selected examples, and address open issues for future developments.