Tsunami damage detection with remote sensing: A review
Tsunamis are rare events compared with the other natural disasters, but once it happens, it
can be extremely devastating to the coastal communities. Extensive inland penetration of …
can be extremely devastating to the coastal communities. Extensive inland penetration of …
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning
This article presents a framework for semi-automated building damage assessment due to
earthquakes from remote-sensing data and other supplementary datasets, while also …
earthquakes from remote-sensing data and other supplementary datasets, while also …
Damaged building detection from post-earthquake remote sensing imagery considering heterogeneity characteristics
Damaged building detection from remote sensing imagery helps to quickly and rapidly
assess losses after an earthquake. In recent years, deep learning technology has become a …
assess losses after an earthquake. In recent years, deep learning technology has become a …
Seismic risk regularization for urban changes due to earthquakes: A case of study of the 2023 turkey earthquake sequence
A Portillo, L Moya - Remote Sensing, 2023 - mdpi.com
Damage identification soon after a large-magnitude earthquake is a major problem for early
disaster response activities. The faster the damaged areas are identified, the higher the …
disaster response activities. The faster the damaged areas are identified, the higher the …
Detecting urban changes using phase correlation and ℓ1-based sparse model for early disaster response: A case study of the 2018 Sulawesi Indonesia earthquake …
Change detection between images is a procedure used in many applications of remote
sensing data. Among these applications, the identification of damaged infrastructures in …
sensing data. Among these applications, the identification of damaged infrastructures in …
Learning from the 2018 Western Japan heavy rains to detect floods during the 2019 Hagibis typhoon
Applications of machine learning on remote sensing data appear to be endless. Its use in
damage identification for early response in the aftermath of a large-scale disaster has a …
damage identification for early response in the aftermath of a large-scale disaster has a …
Novel unsupervised classification of collapsed buildings using satellite imagery, hazard scenarios and fragility functions
Although supervised machine learning classification techniques have been successfully
applied to detect collapsed buildings, there is still a major problem that few publications …
applied to detect collapsed buildings, there is still a major problem that few publications …
Disaster intensity-based selection of training samples for remote sensing building damage classification
Previous applications of machine learning in remote sensing for the identification of
damaged buildings in the aftermath of a large-scale disaster have been successful …
damaged buildings in the aftermath of a large-scale disaster have been successful …
Characteristics of tsunami fragility functions developed using different sources of damage data from the 2018 Sulawesi earthquake and tsunami
We developed tsunami fragility functions using three sources of damage data from the 2018
Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS),(ii) a …
Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS),(ii) a …
Drawback in the change detection approach: False detection during the 2018 western Japan floods
Synthetic aperture radar (SAR) images have been used to map flooded areas with great
success. Flooded areas are often identified by detecting changes between a pair of images …
success. Flooded areas are often identified by detecting changes between a pair of images …