Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment
The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and char...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/11/1711 |
| _version_ | 1797566944054870016 |
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| author | Efstratios Karantanellis Vassilis Marinos Emmanuel Vassilakis Basile Christaras |
| author_facet | Efstratios Karantanellis Vassilis Marinos Emmanuel Vassilakis Basile Christaras |
| author_sort | Efstratios Karantanellis |
| collection | DOAJ |
| description | The increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site. |
| first_indexed | 2024-03-10T19:34:37Z |
| format | Article |
| id | doaj.art-51cc8bdb2fd8471b8c09a27911f90f0a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:34:37Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-51cc8bdb2fd8471b8c09a27911f90f0a2023-11-20T01:51:22ZengMDPI AGRemote Sensing2072-42922020-05-011211171110.3390/rs12111711Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide AssessmentEfstratios Karantanellis0Vassilis Marinos1Emmanuel Vassilakis2Basile Christaras3Laboratory of Engineering Geology and Hydrogeology, Department of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceRemote Sensing Laboratory, Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, 15784 Zografou, GreeceLaboratory of Engineering Geology and Hydrogeology, Department of Geology, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceThe increased development of computer vision technology combined with the increased availability of innovative platforms with ultra-high-resolution sensors, has generated new opportunities and fields for investigation in the engineering geology domain in general and landslide identification and characterization in particular. During the last decade, the so-called Unmanned Aerial Vehicles (UAVs) have been evaluated for diverse applications such as 3D terrain analysis, slope stability, mass movement hazard and risk management. Their advantages of detailed data acquisition at a low cost and effective performance identifies them as leading platforms for site-specific 3D modelling. In this study, the proposed methodology has been developed based on Object-Based Image Analysis (OBIA) and fusion of multivariate data resulted from UAV photogrammetry processing in order to take full advantage of the produced data. Two landslide case studies within the territory of Greece, with different geological and geomorphological characteristics, have been investigated in order to assess the developed landslide detection and characterization algorithm performance in distinct scenarios. The methodology outputs demonstrate the potential for an accurate characterization of individual landslide objects within this natural process based on ultra high-resolution data from close range photogrammetry and OBIA techniques for landslide conceptualization. This proposed study shows that UAV-based landslide modelling on the specific case sites provides a detailed characterization of local scale events in an automated sense with high adaptability on the specific case site.https://www.mdpi.com/2072-4292/12/11/1711landslide assessmentUAV photogrammetryremote sensingobject-based image analysis (OBIA)mass movementssurface deformation |
| spellingShingle | Efstratios Karantanellis Vassilis Marinos Emmanuel Vassilakis Basile Christaras Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment Remote Sensing landslide assessment UAV photogrammetry remote sensing object-based image analysis (OBIA) mass movements surface deformation |
| title | Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment |
| title_full | Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment |
| title_fullStr | Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment |
| title_full_unstemmed | Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment |
| title_short | Object-Based Analysis Using Unmanned Aerial Vehicles (UAVs) for Site-Specific Landslide Assessment |
| title_sort | object based analysis using unmanned aerial vehicles uavs for site specific landslide assessment |
| topic | landslide assessment UAV photogrammetry remote sensing object-based image analysis (OBIA) mass movements surface deformation |
| url | https://www.mdpi.com/2072-4292/12/11/1711 |
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