# Overhead MNIST: A Benchmark Satellite Dataset

arXiv: https://arxiv.org/abs/2102.04266 · HTML: https://mlsanity.com/paper/2102.04266/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 10 of 10)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [Latent Embeddings for Zero-shot Classification](https://mlsanity.com/paper/1603.08895/) (2016)
- [Learning a Deep ConvNet for Multi-label Classification with Partial Labels](https://mlsanity.com/paper/1902.09720/) (2019)
- [Towards the first adversarially robust neural network model on MNIST](https://mlsanity.com/paper/1805.09190/) (2018)
- [Kannada-MNIST: A new handwritten digits dataset for the Kannada language](https://mlsanity.com/paper/1908.01242/) (2019)
- [Discoverability in Satellite Imagery: A Good Sentence is Worth a Thousand Pictures](https://mlsanity.com/paper/2001.05839/) (2020)

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

- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery](https://mlsanity.com/paper/2103.09787/) (2021)
- [Virus-MNIST: A Benchmark Malware Dataset](https://mlsanity.com/paper/2103.00602/) (2021)
- [CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks](https://mlsanity.com/paper/2411.01088/) (2024)
- [Rock Hunting With Martian Machine Vision](https://mlsanity.com/paper/2104.04359/) (2021)
- [Overhead-MNIST: Machine Learning Baselines for Image Classification](https://mlsanity.com/paper/2107.00436/) (2021)

## Abstract (arXiv)

The research presents an overhead view of 10 important objects and follows the general formatting requirements of the most popular machine learning task: digit recognition with MNIST. This dataset offers a public benchmark extracted from over a million human-labelled and curated examples. The work outlines the key multi-class object identification task while matching with prior work in handwriting, cancer detection, and retail datasets. A prototype deep learning approach with transfer learning and convolutional neural networks (MobileNetV2) correctly identifies the ten overhead classes with an average accuracy of 96.7%. This model exceeds the peak human performance of 93.9%. For upgrading satellite imagery and object recognition, this new dataset benefits diverse endeavors such as disaster relief, land use management, and other traditional remote sensing tasks. The work extends satellite benchmarks with new capabilities to identify efficient and compact algorithms that might work on-board small satellites, a practical task for future multi-sensor constellations. The dataset is available on Kaggle and Github.