TY - JOUR
T1 - Landslide detection in real-time social media image streams
AU - Ofli, Ferda
AU - Imran, Muhammad
AU - Qazi, Umair
AU - Roch, Julien
AU - Pennington, Catherine
AU - Banks, Vanessa
AU - Bossu, Remy
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/8
Y1 - 2023/8
N2 - Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response.
AB - Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires active participation. In contrast, as a non-traditional data source, social media has been increasingly used in many disaster response and management studies in recent years. Inspired by this trend, we propose to capitalize on social media data to mine landslide-related information automatically with the help of artificial intelligence techniques. Specifically, we develop a state-of-the-art computer vision model to detect landslides in social media image streams in real-time. To that end, we first create a large landslide image dataset labeled by experts with a data-centric perspective, and then, conduct extensive model training experiments. The experimental results indicate that the proposed model can be deployed in an online fashion to support global landslide susceptibility maps and emergency response.
KW - Data-centric AI
KW - Image classification
KW - Landslide detection
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85160275800&partnerID=8YFLogxK
U2 - 10.1007/s00521-023-08648-0
DO - 10.1007/s00521-023-08648-0
M3 - Article
AN - SCOPUS:85160275800
SN - 0941-0643
VL - 35
SP - 17809
EP - 17819
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 24
ER -