TITLE: Overhead MNIST: A Benchmark Satellite Dataset 

ABSTRACT: 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.
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.
Overhead MNIST: A Benchmark Satellite Dataset
http://arxiv.org/abs/2102.04266v1