Cost-effective Land Cover Classification for Remote Sensing Images

TL;DR

This paper introduces a cost-effective framework for remote sensing land cover classification that reduces cloud computing costs by early stopping of clustering algorithms once a reliable accuracy threshold is reached.

Contribution

It proposes a novel early stopping method for clustering in land cover classification to significantly cut computational costs without sacrificing acceptable accuracy.

Findings

Achieves 85%-99.9% accuracy with only 27.34%-60.83% of the cost of full accuracy.

Saves over $1.59 million in a US land cover case study at 90% accuracy.

Demonstrates substantial cost reduction in cloud-based remote sensing classification.

Abstract

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%.