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

Autor: Pradhan, B, Al-Najjar, HAH, Sameen, MI, Mezaal, MR, Alamri, AM

© 2013 IEEE. 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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::393769d69909ddddbabf2b15dc39eed1
https://hdl.handle.net/10453/146465

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Optimized hierarchical rule-based classification for differentiating shallow and deep-seated landslide using high-resolution lidar data

Autor: Mezaal, MR, Pradhan, B, Shafri, HZM, Mojaddadi, H, Yusoff, ZM

© Springer Nature Singapore Pte Ltd. 2019. Landslide is one of the most devastating natural disasters across the world with serious negative impact on its inhabitants and the environs. Landslide is considered as a type of soil erosion which could be

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::8c5fc5c2744195e59476809646647e72
https://hdl.handle.net/10453/130793

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An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

Autor: Mezaal, MR, Pradhan, B

© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory ma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::983aedf8459aae3fd39ab1a0a936aedd
https://hdl.handle.net/10453/130002

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Data Mining-Aided Automatic Landslide Detection Using Airborne Laser Scanning Data in Densely Forested Tropical Areas

Autor: Pradhan, B, Mezaal, MR

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslid

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::58e8cd65f1d896b3e37dd57c4044bc8a
https://hdl.handle.net/10453/131520

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Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data

Autor: Mezaal, MR, Pradhan, B, Sameen, MI, Shafri, HZM, Yusoff, ZM

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::f095392f3868ce07815090ba713174e6
https://hdl.handle.net/10453/123511

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Optimized rule sets for automatic landslide characteristic detection in a highly vegetated forests

Autor: Pradhan, B, Mezaal, MR

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::8550ab1ba9df560a3f25fadb47249db9
https://hdl.handle.net/10453/126284

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Integration of Ant Colony Optimization and Object-Based Analysis for LiDAR Data Classification

Autor: Sameen, MI, Pradhan, B, Shafri, HZM, Mezaal, MR, Hamid, HB

© 2017 IEEE. Light detection and ranging (LiDAR) data classification provides useful thematic maps for numerous geospatial applications. Several methods and algorithms have been proposed recently for LiDAR data classification. Most studies focused o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______363::f0a4076d88f3c17faec07ad00b656f63
https://hdl.handle.net/10453/123506

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