A dataset of semantic segmentation in earthquake disaster detection based on social media images
Volunteer geographic data,especially from social media,has emerged as a crucial resource for disaster mitigation.Particularly the included imagery can enhance situational awareness during emergencies.However,current applications of this data for disaster relief are primarily limited by a scarcity of annotated images for training,which hinders the efficacy of techniques like machine learning and deep learning,consequently restricting the data's capacity to convey disaster information.Starting from the perspective of practical application,this paper focuses on earthquake disasters as the research subject.By integrating data acquisition,automatic deduplication,semantic annotation,and data augmentation methods,we developed a dataset of semantic segmentation in earthquake disaster detection based on social media images.This dataset mainly consists of original images collected from social media and pseudo-color images with manual semantic annotations.To ensure data quality,multiple annotators work in rotation and cross-check each other's work.This dataset plays a significant role in improving the efficiency of social media data usage and enhancing awareness of disaster reduction efforts.
social mediapicturesearthquake disastersemantic segmentation
万方数据
维普
