# Automatic Dataset Builder for Machine Learning Applications to Satellite Imagery

arXiv: https://arxiv.org/abs/2008.01578 · HTML: https://mlsanity.com/paper/2008.01578/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence](https://mlsanity.com/paper/2305.11891/) (2023)
- [On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery](https://mlsanity.com/paper/2106.15281/) (2021)
- [A speckle filter for Sentinel-1 SAR Ground Range Detected data based on Residual Convolutional Neural Networks](https://mlsanity.com/paper/2104.09350/) (2021)
- [Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification](https://mlsanity.com/paper/2106.11056/) (2021)
- [SEN12-WATER: A New Dataset for Hydrological Applications and its Benchmarking](https://mlsanity.com/paper/2409.17087/) (2024)
- [Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model](https://mlsanity.com/paper/2106.12226/) (2021)
- [Rapid Distributed Fine-tuning of a Segmentation Model Onboard Satellites](https://mlsanity.com/paper/2411.17831/) (2024)

## Abstract (arXiv)

Nowadays the use of Machine Learning (ML) algorithms is spreading in the field of Remote Sensing, with applications ranging from detection and classification of land use and monitoring to the prediction of many natural or anthropic phenomena of interest. One main limit of their employment is related to the need for a huge amount of data for training the neural network, chosen for the specific application, and the resulting computational weight and time required to collect the necessary data. In this letter the architecture of an innovative tool, enabling researchers to create in an automatic way suitable datasets for AI (Artificial Intelligence) applications in the EO (Earth Observation) context, is presented. Two versions of the architecture have been implemented and made available on Git-Hub, with a specific Graphical User Interface (GUI) for non-expert users.