Aerial data collection is well known as an efficient method to study the impact following extreme events. While datasets predominately include images for post-disaster remote sensing analyses, images alone cannot provide detailed geometric information due to a lack of depth or the complexity required to extract geometric details. However, geometric and color information can easily be mined from three-dimensional (3D) point clouds. Scene classification is commonly studied within the field of machine learning, where a workflow follows a pipeline operation to compute a series of engineered features for each point and then points are classified based on these features using a learning algorithm. However, these workflows cannot be directly applied to an aerial 3D point cloud due to a large number of points, density variation, and object appearance. In this study, the point cloud datasets are transferred into a volumetric grid model to be used in the training and testing of 3D fully convolutional network models. The goal of these models is to semantically segment two areas that sustained damage after Hurricane Harvey, which occurred in 2017, into six classes, including damaged structures, undamaged structures, debris, roadways, terrain, and vehicles. These classes are selected to understand the distribution and intensity of the damage. The point clouds consist of two distinct areas assembled using aerial Structure-from-Motion from a camera mounted on an unmanned aerial system. The two datasets contain approximately 5000 and 8000 unique instances, and the developed methods are assessed quantitatively using precision, accuracy, recall, and intersection over union metrics.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning-Based Damage Detection from Aerial SfM Point Clouds


    Beteiligte:


    Erscheinungsdatum :

    2019




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    USING DEEP LEARNING TO IDENTIFY ROAD GEOMETRY FROM POINT CLOUDS

    MOSKOWITZ JEFFREY / BRAGINSKY BORIS / BOUBLIL DAVID et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

    Yijun Liao / Mohammad Ebrahim Mohammadi / Richard L. Wood | DOAJ | 2020

    Freier Zugriff

    Deep Learning based Vehicle Detection in Aerial Imagery

    Sommer, Lars Wilko | TIBKAT | 2022

    Freier Zugriff

    Deep Learning based Vehicle Detection in Aerial Imagery

    Sommer, Lars Wilko | GWLB - Gottfried Wilhelm Leibniz Bibliothek | 2022

    Freier Zugriff

    Deep learning based vehicle detection in aerial imagery

    Sommer, Lars Wilko | TIBKAT | 2021

    Freier Zugriff