A Crowdsourcing-Based Platform for Labelling Remote Sensing Images
Jianghua Zhao, Xuezhi Wang, Yuanchun Zhou · 2020
This paper presents results of the ongoing development of a crowdsourcing platform for labelling remote sensing images. In order to perform fast spatial and temporal retrieval and read data efficiently, this paper proposes an adaptive grid design method to enable the remote sensing images being segmented into tiles, which supports parallel reading and writing, and uses Ceph cluster for storage. To guarantee crowdsourcing results' quality, a quality control mechanism combining expertsourcing and crowdsourcing is proposed. The online labelling system is designed and a prototype has been developed. Users neither have to install any software, nor download large remote sensing images. They can directly label the images by just selecting tags and drawing polygons. By providing such a platform, a large volume of training dataset for remote sensing classification can be obtained.