Incorporating Social Media Sensing and Computer Vision Technologies to Support Wildfire Monitoring

• Published in: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium pp. 2082 - 2085
• Main Authors: Michail, Emmanouil, Bozas, Aristeidis, Stefanopoulos, Dimitrios, Paspalakis, Stavros, Orfanidis, Georgios, Moumtzidou, Anastasia, Gialampoukidis, Ilias, Ioannidis, Konstantinos, Vrochidis, Stefanos, Kompatsiaris, Ioannis
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07-07-2024
• Subjects: Computer vision; Geoscience and remote sensing; Information sharing; Object detection; social media; Social networking (online); Visual analytics; wildfire detection; wildfire management; Wildfires
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS53475.2024.10641848

Social media have evolved into a major source of communication and information sharing, and gradually become impactful in monitoring natural disasters such as wildfires, complementing traditional wildfire monitoring technologies. This paper proposes a comprehensive social media-sensing framework for early wildfire detection, encompassing functionalities like social media crawling, visual analytics, and geolocation, for the analysis of social media posts from the X (former Twitter) platform. Upon analysis, a fire event detection module clusters collected posts into fire events, generating relevant alerts. The framework synergizes with a computer vision algorithm, based on a YOLOv8 architecture, performing object detection on UAV imagery for the detection of individuals in danger in affected areas. The collaborative utilization of social media data and UAV imagery improves situational awareness, by providing information for both the fire incidents and the affected subjects in the area, allowing for a more informed decision making.