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Drones
Special Issues
Drone-Based Photogrammetric Mapping for Change Detection
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Drone-Based Photogrammetric Mapping for Change Detection

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 20 February 2025 | Viewed by 9423

Special Issue Editors

Dr. Giordano Teza

E-Mail Website
Guest Editor
Department of Physics and Astronomy, Alma Mater Studiorum, University of Bologna, Viale Berti Pichat, 6/2 Creti 12, I-40127 Bologna, Italy
Interests: remote sensing (terrestrial laser scanning and structure-from-motion) and application to landslide monitoring; cultural heritage; preservation and medical imaging; 3D modeling; image processing; thermal imaging; GNSS and applications to crustal kinematics; deep learning and applications to time series analysis and medical imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo Fabris

E-Mail Website
Guest Editor
Department of Civil, Environmental and Architectural Engineering-ICEA, University of Padova, 35122 Padova, Italy
Interests: geomatics; digital aerial photogrammetry; digital surface models; deformations monitoring; 3D surveys; land subsidence
Special Issues, Collections and Topics in MDPI journals
Dr. Arianna Pesci

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Viale Berti Pichat, 6/2 Creti 12, I-40127 Bologna, Italy
Interests: terrestrial laser scanner; remote sensing; structure from motion photogrammetry; crustal deformation; geodesy ground deformation; time series; volcanology
Special Issues, Collections and Topics in MDPI journals
Dr. Tina Živec

E-Mail Website
Guest Editor
ELEA iC Engineering and Consulting d.o.o., Dunajska cesta 21, SI-1000 Ljubljana, Slovenia
Interests: remote sensing (laser scanning and photogrammetry); engineering geology; geotechnics; rock mass characterization; tunnelling; engineering geological 3D modelling; BIM (building information modeling)

Special Issue Information

Dear Colleagues,

Photogrammetric mapping based on drones has become a mature technique, even if in continuous development, widely used thanks to the obtainable precision and relatively low costs. The quality of the data now allows change detection by comparing multitemporal models obtained by drone-based surveying or even models obtained in the past using different techniques.

The Special Issue aims to collect original contributions to this topic, focusing on both methodological aspects, including theoretical studies, and applications. The applications can concern every sector, from the natural environment (e.g., landslides, morphological changes of an area, etc.) to urban areas and structures (e.g., single buildings, old towns, bridges, dams, etc.), performed for various motivations (e.g., risk assessment, study of the state of health of a structure, cultural heritage safeguard, cadaster, etc.). The comparison with results obtainable through other techniques is another interesting topic for this issue, with particular regard to analysis of precision, costs, survey and data processing time. This is also interesting because the change detection could be based on comparison of current data from drones and past data obtained by means of other techniques. Moreover, contributions in which drone-based data are used in conjunction with data provided by other techniques (e.g., terrestrial photogrammetry, aerial or terrestrial laser scanning, thermal imaging, GNSS, etc.) are welcome. Papers discussing theoretical models, results obtained from monitoring activities, evolution in space and time of drone-based surveying processes, are also encouraged. Methodological papers whose authors make available the developed software, as supplementary material or placed on a permanent external site, are particularly welcome. Finally, methods based on machine learning techniques are also particularly welcome.

Dr. Giordano Teza
Prof. Massimo Fabris
Ms. Arianna Pesci
Dr. Tina Živec
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV
  • UAS
  • Photogrammetry
  • Structure-from-Motion (SfM)
  • Mapping
  • Surveying
  • Change Detection
  • Pattern Recognition
  • Natural Environment
  • Built Environment

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Published Papers (4 papers)

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15 pages, 19251 KiB  
Article
Mapping Stratigraphy and Artifact Distribution with Unmanned Aerial Vehicle-Based Three-Dimensional Models—A Case Study from the Post Research Area in Northwestern Texas, USA
by Stance Hurst, Eileen Johnson and Doug Cunningham
Drones 2024, 8(11), 684; https://doi.org/10.3390/drones8110684 - 19 Nov 2024
Viewed by 332
Abstract
This study applies UAV-based photogrammetry to map and examine the stratigraphy and archaeological artifact distribution in two localities within the Post research area in northwest Texas. A DJI Inspire 1 UAV equipped with a Zenmuse X5 camera captured nadir and oblique images. These [...] Read more.
This study applies UAV-based photogrammetry to map and examine the stratigraphy and archaeological artifact distribution in two localities within the Post research area in northwest Texas. A DJI Inspire 1 UAV equipped with a Zenmuse X5 camera captured nadir and oblique images. These were processed using Agisoft Metashape to generate 3D models. These models enabled the precise mapping of stratigraphic boundaries, revealing the distinctions between Triassic-age bedrock, Pleistocene-age alluvial deposits, and Holocene-age aeolian sediments. Field surveys from 2022 to 2024 documented over 5000 artifacts with sub-centimeter accuracy, including diagnostic projectile points and ceramics. This research highlights the advantages of UAV-derived 3D models in rapidly and accurately documenting stratigraphy and archaeological data. It demonstrates the value of UAV technology for visualizing landscape-scale processes and artifact contexts, offering a new approach to understanding the interactions between geomorphology and archaeology. The findings contribute to advancing UAV applications in both geomorphological and archaeological research. Full article
(This article belongs to the Special Issue Drone-Based Photogrammetric Mapping for Change Detection)
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16 pages, 7033 KiB  
Article
Multimodal Few-Shot Target Detection Based on Uncertainty Analysis in Time-Series Images
by Mehdi Khoshboresh-Masouleh and Reza Shah-Hosseini
Drones 2023, 7(2), 66; https://doi.org/10.3390/drones7020066 - 17 Jan 2023
Cited by 5 | Viewed by 2901
Abstract
The ability to interpret multimodal data, and map the targets and anomalies within, is important for an automatic recognition system. Due to the expensive and time-consuming nature of multimodal time-series data annotation in the training stage, multimodal time-series image understanding, from drone and [...] Read more.
The ability to interpret multimodal data, and map the targets and anomalies within, is important for an automatic recognition system. Due to the expensive and time-consuming nature of multimodal time-series data annotation in the training stage, multimodal time-series image understanding, from drone and quadruped mobile robot platforms, is a challenging task for remote sensing and photogrammetry. In this regard, robust methods must be computationally low-cost, due to the limited data on aerial and ground-based platforms, yet accurate enough to meet certainty measures. In this study, a few-shot learning architecture, based on a squeeze-and-attention structure, is proposed for multimodal target detection, using time-series images from the drone and quadruped robot platforms with a small training dataset. To build robust algorithms in target detection, a squeeze-and-attention structure has been developed from multimodal time-series images from limited training data as an optimized method. The proposed architecture was validated on three datasets with multiple modalities (e.g., red-green-blue, color-infrared, and thermal), achieving competitive results. Full article
(This article belongs to the Special Issue Drone-Based Photogrammetric Mapping for Change Detection)
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20 pages, 6192 KiB  
Article
A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA
by Brandon Crawford, Erika Swanson, Emily Schultz-Fellenz, Adam Collins, Julian Dann, Emma Lathrop and Damien Milazzo
Drones 2021, 5(2), 25; https://doi.org/10.3390/drones5020025 - 14 Apr 2021
Cited by 5 | Viewed by 3435
Abstract
The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these [...] Read more.
The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions. Full article
(This article belongs to the Special Issue Drone-Based Photogrammetric Mapping for Change Detection)
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Other

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16 pages, 4987 KiB  
Technical Note
Effects of Digital Elevation Model Resolution on Unmanned Aerial Vehicle-Based Topographic Change Detection in Human-Altered Landscapes
by Wen Dai, Jiahui Hu, Bo Wang, Mengtian Fan, Yiyang Zhou and Mengmeng Zhang
Drones 2024, 8(11), 610; https://doi.org/10.3390/drones8110610 - 25 Oct 2024
Viewed by 524
Abstract
UAV-based topographic change detection is widely used in geoscience communities. The change detection involves comparison of two digital elevation models (DEMs) produced by UAV surveys, which are affected by the DEM resolution. Coarse resolution DEMs introduce errors in change detection, but the DEM [...] Read more.
UAV-based topographic change detection is widely used in geoscience communities. The change detection involves comparison of two digital elevation models (DEMs) produced by UAV surveys, which are affected by the DEM resolution. Coarse resolution DEMs introduce errors in change detection, but the DEM resolution effect remains poorly understood. Moreover, effective strategies for mitigating the resolution effect have yet to be investigated. This study generated UAV-based DEMs at resolutions ranging from 0.1 m to 10 m with various resampling methods. The impact of DEM resolution on topographic change detection was then evaluated by analyzing the difference of DEM (DoD) and volume budget errors with indices such as mean error (ME), standard deviation (STD), and Moran’s I. The results from two human-altered landscapes showed that the random errors of DoD increase rapidly with the DEM resolution coarsening, while DoD systematic errors (spatial distribution of errors) become stable after 4 m resolution. The volume budget errors also increase with DEM coarsening. Coarser resolution DEMs tend to underestimate the volume budget (gross erosion, gross deposition, and net changes). Moreover, selecting an appropriate method for generating DEM is beneficial in decreasing the errors caused by the resolution effect. Among the seven methods (MAX, MIN, MEAN, BIL, NEAR, NEB, and TIN), the BIL method is optimum for mitigating both DoD and volume errors. The NEAR, NEB, and TIN methods are equivalent, and they are superior to the aggregation methods (MAX, MIN, MEAN). The slope of DoD (SDoD) should be considered when selecting a resolution for change detection. Large errors tend to appear in areas with large SDoD and vice versa. Coarse resolution DEMs are tolerable in areas with low SDoD, while high resolution DEMs are necessary in areas with large SDoD. Full article
(This article belongs to the Special Issue Drone-Based Photogrammetric Mapping for Change Detection)
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