InSAR Crowdsourcing Annotation System With Volunteers Uploaded Photographs: Toward a Hazard Alerting System

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Xiong, Siting, Wang, Chisheng, Chen, Chunjing, Zhang, Bochen, Li, Qingquan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Buildings; Crowdsourcing; Deformation; Deformation effects; Deformation recognition and classification; Displacement; Hazards; In situ measurement; Interferometric synthetic aperture radar; interferometric synthetic aperture radar (InSAR); Land acquisition; Prototypes; SAR (radar); Strain; Synthetic aperture radar; Synthetic aperture radar interferometry; Task analysis; time-series deformation; volunteered geographic information (VGI)
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2021.3134311

Interferometric synthetic aperture radar (InSAR) has been more and more applied in acquiring long-term deformation of land surface in a large coverage and is becoming a routine investigation technique. Validation of the InSAR results depends largely on the in situ measurements. These measurements are usually point-wise and of high cost, as many sensors need to be set up for the long-term monitoring. In applications associated with a large area, qualitative and low-cost validation may be more necessary at the first place, which is still a challenge. In the recent decade, the crowdsourcing and volunteered geographic information (VGI) have been more and more accepted in the field of geoinformatics. Inspired by these, this letter proposes an InSAR crowdsourcing annotation system to integrate the InSAR displacements and the photographs uploaded by volunteers. We processed 119 Sentinel-1A data ranging from 2017 to 2021 to derive long-term displacements. Then, based on the InSAR displacements, task areas were selected and published to public via the system. Volunteers online accepted the task and uploaded photographs, indicating land displacements. In a coastal city, Shenzhen of China, 135 task areas were selected and published in total, and 1742 useful photographs were uploaded. The uploaded photographs were then inspected to validate and analyze the InSAR results. Post-analysis found some high correlation between the uploaded photographs with the InSAR alerting displacements. The proposed system is a prototype, and its interface and functions can be further extended in the future toward an effective and efficient alerting system for risking land deformations.