Scale Drone Mapping on K8S: Auto-scale Drone Imagery Processing on Kubernetes-orchestrated On-premise Cloud-computing Platform

Vithlani, Hemang Narendra; Dogotari, Marcel; Lam, Olee Hoi Ying; Prüm, Moritz; Melville, Bethany; Zimmer, Fränk; Becker, Rolf

doi:10.5220/0009816003180325

Cited by 5 publications

(2 citation statements)

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“…These technological advances offer significant potential for producing orthomosaic maps using drones. Open-source solutions, such as OpenDroneMap, are available to generate orthomosaic maps, point clouds, 3D models, and other data types [9,10]. Nevertheless, selecting the most appropriate combination of algorithms for a specific problem and choosing one that ensures accuracy and rapid registration are characterized by empirical trials and errors.…”

Section: Research Objectivesmentioning

confidence: 99%

Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration

Hwang,

Lee,

et al. 2023

Preprint

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This study presents a method for the rapid and accurate generation of two-dimensional (2D) orthomosaic maps using selected image data collected by drone-captured video. The focus is on developing a real-time method capable of creating maps more quickly than image selection. The scale-invariant feature transform (SIFT) algorithm is applied to drone images to extract features in various scale regions. For feature point matching, a matching method based on the fast library for approximate nearest neighbors (FLANN) was adopted. A comparison of the computational speed of the FLANN with that of the k-nearest neighbors (KNN) and brute force matcher during the matching process revealed FLANN's superior capability for real-time data processing. The random sample consensus (RANSAC) algorithm was employed to enhance the accuracy of the matching by removing outliers, effectively identifying and eliminating mismatches, and reinforcing the reliability of feature point matching. The combination of SIFT, FLANN, and RANSAC algorithms demonstrates the capacity to process drone-captured image data in real time, facilitating the generation of precise 2D orthomosaic maps. The proposed method was assessed and validated using imagery obtained as the drone executed curvilinear and straight flight paths, confirming its accuracy and operational efficiency concurrently with the image capture process.

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Section: Research Objectivesmentioning

confidence: 99%

Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration

Hwang,

Lee,

et al. 2023

Preprint

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“…K8s is commonly known as the standard for container orchestration based on its widespread adoption in a hybrid cloud environment [32]. A parallel cloud-computing platform was implemented using the open-source toolkit OpenDroneMap for unmanned aerial vehicle (UAV) imagery processing in an onpremises cluster on K8s [33]. A computing framework based on free and open-source technology for accessing data and processing facilities was provided by the Eurac Research Sentinel Alpine Obseratory, which carries out cloud computing on an open-source platform of OpenNebula and K8s, big data analytics on a Rasdaman server, and web-based Python development on Jupyter [34].…”

Section: Introductionmentioning

confidence: 99%

A Scalable Computing Resources System for Remote Sensing Big Data Processing Using GeoPySpark Based on Spark on K8s

2022

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As a result of Earth observation (EO) entering the era of big data, a significant challenge relating to by the storage, analysis, and visualization of a massive amount of remote sensing (RS) data must be addressed. In this paper, we proposed a novel scalable computing resources system to achieve high-speed processing of RS big data in a parallel distributed architecture. To reduce data movement among computing nodes, the Hadoop Distributed File System (HDFS) is established on nodes of K8s, which are also used for computing. In the process of RS data analysis, we innovatively use the tile-oriented programming model instead of the traditional strip-oriented or pixel-oriented approach to better implement parallel computing in a Spark on Kubernetes (K8s) cluster. A large RS raster layer can be abstracted as a user-defined tile format of any size, so that a whole computing task can be divided into multiple distributed parallel tasks. The computing resources applied by users would be immediately assigned in the Spark on K8s cluster by simply configuring and initializing SparkContext through a web-based Jupyter notebook console. Users can easily query, write, or visualize data in any box size from the catalog module in GeoPySpark. In summary, the system proposed in this study can provide a distributed scalable resources system for assembling big data storage, parallel computing, and real-time visualization.

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Generating UAV high-resolution topographic data within a FOSS photogrammetric workflow using high-performance computing clusters

Salandra

Miniello

Nicotri

et al. 2021

International Journal of Applied Earth Observation and Geoinfor

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Scale Drone Mapping on K8S: Auto-scale Drone Imagery Processing on Kubernetes-orchestrated On-premise Cloud-computing Platform

Cited by 5 publications

References 0 publications

Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration

Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration

A Scalable Computing Resources System for Remote Sensing Big Data Processing Using GeoPySpark Based on Spark on K8s

Generating UAV high-resolution topographic data within a FOSS photogrammetric workflow using high-performance computing clusters

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