  Land cover maps are of vital importance to various fields such as land use
policy development, ecosystem services, urban planning and agriculture
monitoring, which are mainly generated from remote sensing image classification
techniques. Traditional land cover classification usually needs tremendous
computational resources, which often becomes a huge burden to the remote
sensing community. Undoubtedly cloud computing is one of the best choices for
land cover classification, however, if not managed properly, the computation
cost on the cloud could be surprisingly high. Recently, cutting the unnecessary
computation long tail has become a promising solution for saving the cost in
the cloud. For land cover classification, it is generally not necessary to
achieve the best accuracy and 85% can be regarded as a reliable land cover
classification. Therefore, in this paper, we propose a framework for
cost-effective remote sensing classification. Given the desired accuracy, the
clustering algorithm can stop early for cost-saving whilst achieving sufficient
accuracy for land cover image classification. Experimental results show that
achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud
computation cost for achieving a 100% accuracy. To put it into perspective, for
the US land cover classification example, the proposed approach can save over
$1,593,490.18 for the government in each single-use when the desired accuracy
is 90%.
