Classification of rock slope cavernous weathering on UAV photogrammetric point clouds: The example of Hegra (UNESCO World Heritage Site, Kingdom of Saudi Arabia)

Beni, Tommaso; Nava, Lorenzo; Gigli, Giovanni; Frodella, William; Catani, Filippo; Casagli, Nicola; Gallego, José Ignacio; Margottini, Claudio; Spizzichino, Daniele

doi:10.1016/j.enggeo.2023.107286

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…O CloudCompare oferece recursos robustos para a classificação e processamento rápido de nuvens de pontos [17]. O CloudCompare aborda um dos principais desafios no processamento de nuvens de pontos -a limpeza de dados, funcionalidade que aumenta consideravelmente a qualidade e a confiabilidade dos dados.…”

Section: Processamento Da Nuvem De Pontosunclassified

Utilização de drones na gestão de instalações de armazenamento de água e de resíduos industriais

Ribeirinho,

Ferreira,

Tavares

2024

5º Congresso Português De Building Information Modelling Volume 1: ptBIM

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Section: Processamento Da Nuvem De Pontosunclassified

Utilização de drones na gestão de instalações de armazenamento de água e de resíduos industriais

Ribeirinho,

Ferreira,

Tavares

2024

5º Congresso Português De Building Information Modelling Volume 1: ptBIM

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“…Numerous research has recently combined optical and multispectral data with deep learning (DL) models for landslide detection (Novellino et al, 2024). These approaches range from utilizing crowdsourced data (Catani, 2021) and Unmanned Aerial Vehicles (UAVs) (Beni et al, 2023;Dai et al, 2023a) to analyzing LIDAR (Fang et al, 2022), and satellite imagery (Bhuyan et al, 2023;Prakash et al, 2021). Some studies have also investigated the incorporation of morphological factors alongside satellite data for DL-based landslide detection (Meena et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 SAR-based Globally Distributed Landslide Detection by Deep Neural Networks

Nava,

Mondini,

Bhuyan

et al. 2024

Preprint

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Efficient response to large and widespread multiple landslide events (MLEs) demands rapid and effective landslide detection. Despite extensive efforts using optical remotely sensed imagery, limitations in global, day & night, and all-weather operational capabilities remain. To address these gaps, we introduce an approach that harnesses Deep Neural Networks (DNNs) and Synthetic Aperture Radar (SAR) backscatter data. This approach is designed through the analysis of 11 earthquake-induced MLEs, encompassing approximately 73 thousand landslides that occurred worldwide in a variety of different geo-settings. We test the reproducibility of the model results on unseen earthquake-induced landslides that occurred in Sumatra and Haiti. The top-performing model achieved a test F1-score of 82% in rapid assessment, indicating significant progress compared to previous attempts. The approach harnesses the cloud computing resources of Google Earth Engine for Sentinel-1 SAR image acquisition and processing, complemented by local computing resources to utilize advanced image classification DNNs capabilities. Through explainable artificial intelligence, our study underscores the efficacy of change detection bands in their superior discriminative capacity to delineate landslide features, surpassing the utilization of backscatter data alone. Moreover, we observe an improved ability to detect landslides within multi-temporal information stacks as opposed to single post-event SAR images. Finally, we introduce the SAR-LRA Tool in its Beta version, providing a valuable resource for rapid and comprehensive all-weather global landslide assessment. The systematic use of the Tool promises to facilitate the timely response to future MLEs. Our work establishes a robust foundation for future research endeavors, wherein SAR and DNNs can be harnessed to identify natural hazards and/or specific earth surface changes in mountainous regions. Given the frequent and increasing occurrence of MLEs, the development of a robust modeling approach is imperative to timely assess the spatial distribution of these phenomena. This research will pave the way for efficient rapid assessment of MLEs in the future.

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A Multilevel Classification Strategy for the Identification of Discontinuities from 3D Point Clouds of Complicated Rock Surfaces

Ma,

Zuo,

Qiu

et al. 2024

Rock Mech Rock Eng

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Classification of rock slope cavernous weathering on UAV photogrammetric point clouds: The example of Hegra (UNESCO World Heritage Site, Kingdom of Saudi Arabia)

Cited by 6 publications

References 49 publications

Utilização de drones na gestão de instalações de armazenamento de água e de resíduos industriais

Utilização de drones na gestão de instalações de armazenamento de água e de resíduos industriais

Sentinel-1 SAR-based Globally Distributed Landslide Detection by Deep Neural Networks

A Multilevel Classification Strategy for the Identification of Discontinuities from 3D Point Clouds of Complicated Rock Surfaces

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