Abstract—Landslides are a significant hazard to property
and livelihoods, causing millions of dollars worth of damage
annually throughout the world, but especially in tropical
regions such as Malaysia. Automated or semi-automated
detection of landslides from aerial or satellite imagery and
generating landslide susceptibility or hazard map are two of the
main research topics in landslide research. In this paper, we
propose a probability map approach in detecting possible
landslide regions from satellite or aerial images. The detected
landslides, tabulated as landslide inventory map, will be useful
as the ground truth for evaluating landslide susceptibility map,
or even used as one of the causative factors for the susceptibility
map itself. The proposed probability map is computed using
only colour information, but demonstrated very promising
performance in locating potential landslide regions; thus
provides a strong platform to locate actual landslides by
incorporating texture and shape features in the future.
Index Terms—Landslide detection, landslide inventory, landslide probability map.
Mohammad Faizal Ahmad Fauzi, Sin Liang Lim , and Wooi Nee Tan are with the Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
Agustinus Deddy Arief Wibowo was with the Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia (e-mail: email@example.com).
Cite: Mohammad Faizal Ahmad Fauzi, Agustinus Deddy Arief Wibowo, Sin Liang Lim, and Wooi Nee Tan, "On the Detection of Possible Landslides in Post-Event Satellite Images: A Probability Map Approach," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 325-328, 2015.