Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
M Sheykhmousa, M Mahdianpari… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …
classification during the past two decades. Among these machine learning algorithms …
Pathways and challenges of the application of artificial intelligence to geohazards modelling
The application of artificial intelligence (AI) and machine learning in geohazard modelling
has been rapidly growing in recent years, a trend that is observed in several research and …
has been rapidly growing in recent years, a trend that is observed in several research and …
Selecting critical features for data classification based on machine learning methods
Feature selection becomes prominent, especially in the data sets with many variables and
features. It will eliminate unimportant variables and improve the accuracy as well as the …
features. It will eliminate unimportant variables and improve the accuracy as well as the …
Change detection techniques with synthetic aperture radar images: Experiments with random forests and Sentinel-1 observations
This work aims to clarify the potential of incoherent and coherent change detection (CD)
approaches for detecting and monitoring ground surface changes using sequences of …
approaches for detecting and monitoring ground surface changes using sequences of …
Transferability of convolutional neural network models for identifying damaged buildings due to earthquake
The collapse of buildings caused by earthquakes can lead to a large loss of life and
property. Rapid assessment of building damage with remote sensing image data can …
property. Rapid assessment of building damage with remote sensing image data can …
Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 sulawesi earthquake and tsunami in Palu, Indonesia
This work presents a detailed analysis of building damage recognition, employing multi-
source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A …
source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A …
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 …
An artificial intelligence application for post-earthquake damage mapping in Palu, central Sulawesi, Indonesia
A Mw 7.4 earthquake hit Donggala County, Central Sulawesi Province, Indonesia, on 28
September 2018, triggering a tsunami and liquefaction in Palu City and Donggala. Around …
September 2018, triggering a tsunami and liquefaction in Palu City and Donggala. Around …
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 …
3D gray level co-occurrence matrix and its application to identifying collapsed buildings
With the remarkable progress in access to remote sensing imagery data, nowadays
research very often utilizes more than one image. We are often able to use multitemporal …
research very often utilizes more than one image. We are often able to use multitemporal …