  The ever-growing volume of satellite imagery data presents a challenge for
industry and governments making data-driven decisions based on the timely
analysis of very large data sets. Commonly used deep learning algorithms for
automatic classification of satellite images are time and resource-intensive to
train. The cost of retraining in the context of Big Data presents a practical
challenge when new image data and/or classes are added to a training corpus.
Recognizing the need for an adaptable, accurate, and scalable satellite image
chip classification scheme, in this research we present an ensemble of: i) a
slow to train but high accuracy vision transformer; and ii) a fast to train,
low-parameter convolutional neural network. The vision transformer model
provides a scalable and accurate foundation model. The high-speed CNN provides
an efficient means of incorporating newly labelled data into analysis, at the
expense of lower accuracy. To simulate incremental data, the very large
(~400,000 images) So2Sat LCZ42 satellite image chip dataset is divided into
four intervals, with the high-speed CNN retrained every interval and the vision
transformer trained every half interval. This experimental setup mimics an
increase in data volume and diversity over time. For the task of automated
land-cover/land-use classification, the ensemble models for each data increment
outperform each of the component models, with best accuracy of 65% against a
holdout test partition of the So2Sat dataset. The proposed ensemble and
staggered training schedule provide a scalable and cost-effective satellite
image classification scheme that is optimized to process very large volumes of
satellite data.
