Výsledky vyhledávání - "Husam A. H. Al-Najjar"

Akademický článek

Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos

Autor: Biswajeet Pradhan, Husam A. H. Al-Najjar, Maher Ibrahim Sameen, Mustafa Ridha Mezaal, Abdullah M. Alamri

Publikováno v: IEEE Access, Vol 8, Pp 121942-121954 (2020)

This study proposes a new landslide detection technique that is semi-automated and based on a saliency enhancement approach. Unlike most of the landslide detection techniques, the approach presented in this paper is simple yet effective and does not

Externí odkaz: https://doaj.org/article/09a152c2814c4ce785c3ff005b01deb6

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Akademický článek

A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides

Autor: Biswajeet Pradhan, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, Daichao Sheng, Abdullah M. Alamri, Hyuck-Jin Park

Publikováno v: Remote Sensing, Vol 13, Iss 22, p 4521 (2021)

Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of prede

Externí odkaz: https://doaj.org/article/d9f6413b0fc947bcb2897e97b34baabd

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Akademický článek

A New Integrated Approach for Landslide Data Balancing and Spatial Prediction Based on Generative Adversarial Networks (GAN)

Autor: Husam A. H. Al-Najjar, Biswajeet Pradhan, Raju Sarkar, Ghassan Beydoun, Abdullah Alamri

Publikováno v: Remote Sensing, Vol 13, Iss 19, p 4011 (2021)

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory/data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality lan

Externí odkaz: https://doaj.org/article/f8538c425b9c4e3a9dd66a92b749a4c2

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Akademický článek

Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation

Autor: Husam A. H. Al-Najjar, Biswajeet Pradhan, Bahareh Kalantar, Maher Ibrahim Sameen, M. Santosh, Abdullah Alamri

Publikováno v: Remote Sensing, Vol 13, Iss 16, p 3281 (2021)

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input

Externí odkaz: https://doaj.org/article/e21f445bdba943e9a2dc31070015afa4

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Akademický článek

Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

Autor: Bahareh Kalantar, Naonori Ueda, Husam A. H. Al-Najjar, Alfian Abdul Halin

Publikováno v: Remote Sensing, Vol 12, Iss 21, p 3529 (2020)

In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earth

Externí odkaz: https://doaj.org/article/7b7fe14395744d87b68f6c997444c3ba

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Akademický článek

Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks

Autor: Biswajeet Pradhan, Husam A. H. Al-Najjar, Maher Ibrahim Sameen, Ivor Tsang, Abdullah M. Alamri

Publikováno v: Remote Sensing, Vol 12, Iss 10, p 1676 (2020)

Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carr

Externí odkaz: https://doaj.org/article/225ae7d0ffda4ebebf6171122bc2b601

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Akademický článek

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Autor: Husam A. H. Al-Najjar, Bahareh Kalantar, Biswajeet Pradhan, Vahideh Saeidi, Alfian Abdul Halin, Naonori Ueda, Shattri Mansor

Publikováno v: Remote Sensing, Vol 11, Iss 12, p 1461 (2019)

In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are n

Externí odkaz: https://doaj.org/article/481942b0e7ab45edac0350f6fe40fb1b

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Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

Autor: Husam Abdulrasool H. Al-Najjar, Biswajeet Pradhan

Publikováno v: Geoscience Frontiers, Vol 12, Iss 2, Pp 625-637 (2021)

In recent years, landslide susceptibility mapping has substantially improved with advances in machine learning. However, there are still challenges remain in landslide mapping due to the availability of limited inventory data. In this paper, a novel

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53d1dcbe9517fbd6dba3f4afbc3205f0
http://www.sciencedirect.com/science/article/pii/S1674987120302024

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