Review of Photogrammetric and Lidar Applications of UAV

Kovanič, Ľudovít; Topitzer, Branislav; Peťovský, Patrik; Blišťan, Peter; Gergeľová, Marcela Bindzárová; Blišťanová, Monika

doi:10.3390/app13116732

Cited by 12 publications

(3 citation statements)

References 175 publications

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“…Unmanned Aerial Vehicles (UAV), or more simply drones, are establishing as one of the most rapidly developing technologies in recent years (Kovanič et al 2023). As evidence of this, a few years ago the Massachusetts Institute of Technology (MIT), considered by many to be the most authoritative university in the field of technology, highlighted the UAV in its annual ranking of technologies with the greatest development potential.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2024

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Papua, known as one of the wettest regions in the world with annual precipitation ranging from 2400 to 4500 mm, faces a high risk of flooding, especially during La Niña, as observed in 2018 and 2022. Conversely, the region also experiences forest fires, mainly in the southern areas of Papua, during periods of extreme dry conditions brought about by El Niño events, as seen in 2002, 2004, and 2015. Given the increasing frequency of extreme climate events in the context of climate change, understanding the impact of global climate phenomena such as El Niño Southern Oscillation (ENSO) on Papua is crucial. This research aims to analyze the influence of ENSO and the Indian Ocean Dipole (IOD) on forest fires and flood risk in Papua, Indonesia. The analysis of forest fires utilizes MODIS hotspot and ERA5 precipitation data, employing quantitative modeling techniques such as Lasso and Elastic Net Regression. It integrates both ENSO and IOD indices into the precipitation indicators. In contrast, flood analysis is carried out through distribution and joint pattern analysis. The Elastic Net Regression yields promising results in modeling, with more than 96% of the tested models successfully predicting the total annual hotspots for each year, achieving an R-squared value of 90%. This suggests that the method and algorithm used can serve as a robust model for early hotspot prediction in the analyzed area. The warm phases of ENSO and IOD consistently exhibit a positive correlation with the dry season. However, the cold phases of these phenomena do not significantly impact heavy and extreme precipitation indicators in the studied region. The flood analysis reveals that La Niña has only a slight effect on all three precipitation patterns in the analyzed area, primarily by increasing the risk of extreme precipitation indicators. Conversely, negative IOD demonstrates inconsistency across all three precipitation patterns in Papua.

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Section: Introductionmentioning

confidence: 99%

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2024

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“…In the last decade, different remote sensing systems such as visible light and multispectral cameras, Light Detection and Ranging (LiDAR) have been used as payloads of unmanned aerial vehicles (UAVs) or drones (Akturk and Altunel, 2019;Carpenter et al, 2023;Zhang and Zhu, 2023). Nevertheless, visible light cameras continue to maintain their prevalence and dominance among the available options for DEM production, primarily attributed to their cost-effectiveness (Bi et al, 2021;Giordan et al, 2020;Kovanič et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

Doğruluk,

Yalçın

2023

International Journal of Environment and Geoinformatics

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Surface modeling constitutes is a crucial aspect in numerous engineering inquiries and earth observation endeavors. In contemporary times, the acquisition of geospatial data essential for the digital representation of local regions is increasingly facilitated through drone-based methodologies, supplanting conventional terrestrial data gathering techniques. The market presently hosts a plethora of cost-effective, "ready-to-fly" unmanned aerial vehicles (UAVs), offering users the capability to generate photogrammetric outputs, including high geometric precision Digital Elevation Models (DEMs). Moreover, modularly structured drone development kits, designed for multifarious applications, are readily accessible for purchase. These drone kits offer an economically advantageous platform that users can customize to suit their specific needs. Nevertheless, the geometric precision of DEMs created using these kits hinges upon the capabilities of the imaging and navigation systems, in addition to the stabilization of the platform during autonomous flight. In this study, using a drone development kit and a commercial drone, simultaneous image acquisition was performed for the same study area and two different DEMs were produced. The efficacy of the DEM generated using the drone development kit was assessed through a comparative analysis with the DEM obtained from a commercial drone. In addition, geometric accuracy assessment was conducted for both DEMs using ground control points. The findings reveal the usability of drone development kits in precision DEM production, as well as their limitations.

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“…Point clouds are nowadays a widely used source of spatial information applicable to a wide range of engineering and scientific applications, both outdoors and indoors. Threedimensional laser scanning and structure from motion-multi-view stereo (SfM-MVS) photogrammetry are the two most common methods of their acquisition [1]. Three-dimensional laser scanning can be performed from stationary terrestrial [2] as well as mobile [3] and, recently, unmanned aerial vehicle (UAV) platforms [4][5][6] or airplanes [7,8].…”

Section: Introductionmentioning

confidence: 99%

Color-Based Point Cloud Classification Using a Novel Gaussian Mixed Modeling-Based Approach versus a Deep Neural Network

Štroner,

Urban,

Línková

2023

Remote Sensing

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The classification of point clouds is an important research topic due to the increasing speed, accuracy, and detail of their acquisition. Classification using only color is basically absent in the literature; the few available papers provide only algorithms with limited usefulness (transformation of three-dimensional color information to a one-dimensional one, such as intensity or vegetation indices). Here, we proposed two methods for classifying point clouds in RGB space (without using spatial information) and evaluated the classification success since it allows a computationally undemanding classification potentially applicable to a wide range of scenes. The first is based on Gaussian mixture modeling, modified to exploit specific properties of the RGB space (a finite number of integer combinations, with these combinations repeated in the same class) to automatically determine the number of spatial normal distributions needed to describe a class (mGMM). The other method is based on a deep neural network (DNN), for which different configurations (number of hidden layers and number of neurons in the layers) and different numbers of training subsets were tested. Real measured data from three sites with different numbers of classified classes and different “complexity” of classification in terms of color distinctiveness were used for testing. Classification success rates averaged 99.0% (accuracy) and 96.2% (balanced accuracy) for the mGMM method and averaged 97.3% and 96.7% (balanced accuracy) for the DNN method in terms of the best parameter combinations identified.

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Review of Photogrammetric and Lidar Applications of UAV

Cited by 12 publications

References 175 publications

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Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

Color-Based Point Cloud Classification Using a Novel Gaussian Mixed Modeling-Based Approach versus a Deep Neural Network

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