Cost-effective land cover classification for remote sensing images

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 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%.

Validation of Unmanned Aerial Vehicle Photogrammetry for Landslide Mapping

“…This value indicates that Polines 01-GD with Go Pro Hero 3 camera can be used as an instrument for 3D mapping. Because the minimum accuracy required for a land surface classification survey is 85% [40]. This is because the accuracy of aerial images, both Polines 01-GD and the DJI Phantom 4 Pro, meet the minimum criteria.…”

Spatiotemporal Analysis and Prediction of Urban Land Use/Land Cover Changes Using a Cellular Automata and Novel Patch-Generating Land Use Simulation Model: A Study of Zhejiang Province, China

“…Recent environmental studies have employed remotely sensed data using various geospatial techniques to examine the change dynamics of LULC [25]. Such methods provide a cost-effective approach for the spatial and quantitative exploration of land use changes using earth observation data [26,27]. Time-series satellite data analysis, in particular, offers an excellent platform for understanding and predicting urban land use changes [28,29].…”