CoLabel: a collaborative labeling platform for remote sensing imagery

With the rapid advancement of artificial intelligence in the interpretation of remote sensing imagery, there is a growing demand for a large volume of remote sensing training data in both academic and industrial sectors. However, the traditional approach to data annotation takes place in a standalone environment, resulting in low work efficiency. This creates an immediate need to develop a web platform that facilitates collaborative annotations, allowing for the fully utilization of collective intelligence from experts in the field. This paper introduces CoLabel, a user-friendly, web-based platform designed to streamline the process of labeling remote sensing images. CoLabel enables multiple users to collaboratively label remote sensing images through its intuitive interface. It supports various labeling tasks, including object detection and semantic segmentation. To ensure the quality of training data before publication, the platform incorporates a quality review mechanism that conducts thorough inspections of annotated data. By providing robust training data support, CoLabel contributes to the research and applications in the field of intelligent interpretation of remote sensing.