# Overhead-MNIST: Machine Learning Baselines for Image Classification

arXiv: https://arxiv.org/abs/2107.00436 · HTML: https://mlsanity.com/paper/2107.00436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [CapsNet comparative performance evaluation for image classification](https://mlsanity.com/paper/1805.11195/) (2018)
- [Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST)](https://mlsanity.com/paper/1811.08278/) (2018)
- [Overhead MNIST: A Benchmark Satellite Dataset](https://mlsanity.com/paper/2102.04266/) (2021)
- [A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy](https://mlsanity.com/paper/1911.07644/) (2019)

## Abstract (arXiv)

Twenty-three machine learning algorithms were trained then scored to establish baseline comparison metrics and to select an image classification algorithm worthy of embedding into mission-critical satellite imaging systems. The Overhead-MNIST dataset is a collection of satellite images similar in style to the ubiquitous MNIST hand-written digits found in the machine learning literature. The CatBoost classifier, Light Gradient Boosting Machine, and Extreme Gradient Boosting models produced the highest accuracies, Areas Under the Curve (AUC), and F1 scores in a PyCaret general comparison. Separate evaluations showed that a deep convolutional architecture was the most promising. We present results for the overall best performing algorithm as a baseline for edge deployability and future performance improvement: a convolutional neural network (CNN) scoring 0.965 categorical accuracy on unseen test data.