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Photogrammetry and Remote Sensing in Environmental and Engineering Applications (Second Edition)

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Dr. Wojciech Drzewiecki

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Guest Editor

Department of Photogrammetry, Remote Sensing of Environment and Spatial Engineering, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Science and Technology, Krakow, Poland
Interests: image processing; image classification; image analysis; machine learning; predictive models; land-use and land cover change monitoring; geoinformation; spatial analysis
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Dr. Antoni Rzonca

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Guest Editor Assistant

Department of Photogrammetry, Remote Sensing of Environment and Spatial Engineering, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Science and Technology, Krakow, Poland
Interests: lidar data processing; deep learning matching; image and lidar data integration; enhancement of UAV data accuracy; visual and thermal data integration

Special Issue Information

Dear Colleagues,

For a long time, photogrammetry and remote sensing have been considered valuable and often irreplaceable tools in various applications. Nowadays, the improved accuracy and reliability of the data acquired together with faster and more efficient technologies for data processing and analysis open new horizons for their implementation. As a result, photogrammetry and remote sensing tools have successfully been used to solve applied research problems emerging in many areas.

The previous Special Issue focused on broadly defined environmental and/or engineering applications of photogrammetry and remote sensing, which attracted considerable interest. For this reason, we would like once again to invite research papers presenting innovative approaches for the implementation of these technologies in practice.

Submissions may be related to the use of close-range, terrestrial, aerial, and/or satellite-based sensors of various kinds. Studies dedicated to the integration of photogrammetric and remote sensing techniques and/or novel methods of their application are especially welcomed.

Dr. Wojciech Drzewiecki
Guest Editor

Dr. Antoni Rzonca
Guest Editor Assistant

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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.

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22 pages, 15219 KB

Open AccessArticle

Integrating UAS Remote Sensing and Edge Detection for Accurate Coal Stockpile Volume Estimation

by Sandeep Dhakal, Ashish Manandhar, Ajay Shah and Sami Khanal

Remote Sens. 2025, 17(18), 3136; https://doi.org/10.3390/rs17183136 - 10 Sep 2025

Viewed by 723

Abstract

Accurate stockpile volume estimation is essential for industries that manage bulk materials across various stages of production. Conventional ground-based methods such as walking wheels, total stations, Global Navigation Satellite Systems (GNSSs), and Terrestrial Laser Scanners (TLSs) have been widely used, but often involve significant safety risks, particularly when accessing hard-to-reach or hazardous areas. Unmanned Aerial Systems (UASs) provide a safer and more efficient alternative for surveying irregularly shaped stockpiles. This study evaluates UAS-based methods for estimating the volume of coal stockpiles at a storage facility near Cadiz, Ohio. Two sensor platforms were deployed: a Freefly Alta X quadcopter equipped with a Real-Time Kinematic (RTK) Light Detection and Ranging (LiDAR, active sensor) and a WingtraOne UAS with Post-Processed Kinematic (PPK) multispectral imaging (optical, passive sensor). Three approaches were compared: (1) LiDAR; (2) Structure-from-Motion (SfM) photogrammetry with a Digital Surface Model (DSM) and Digital Terrain Model (DTM) (SfM–DTM); and (3) an SfM-derived DSM combined with a kriging-interpolated DTM (SfM–intDTM). An automated boundary detection workflow was developed, integrating slope thresholding, Near-Infrared (NIR) spectral filtering, and Canny edge detection. Volume estimates from SfM–DTM and SfM–intDTM closely matched LiDAR-based reference estimates, with Root Mean Square Error (RMSE) values of 147.51 m³ and 146.18 m³, respectively. The SfM–intDTM approach achieved a Mean Absolute Percentage Error (MAPE) of ~2%, indicating strong agreement with LiDAR and improved accuracy compared to prior studies. A sensitivity analysis further highlighted the role of spatial resolution in volume estimation. While RMSE values remained consistent (141–162 m³) and the MAPE below 2.5% for resolutions between 0.06 m and 5 m, accuracy declined at coarser resolutions, with the MAPE rising to 11.76% at 10 m. This emphasizes the need to balance the resolution with the study objectives, geographic extent, and computational costs when selecting elevation data for volume estimation. Overall, UAS-based SfM photogrammetry combined with interpolated DTMs and automated boundary extraction offers a scalable, cost-effective, and accurate approach for stockpile volume estimation. The methodology is well-suited for both the high-precision monitoring of individual stockpiles and broader regional-scale assessments and can be readily adapted to other domains such as quarrying, agricultural storage, and forestry operations. Full article

(This article belongs to the Special Issue Photogrammetry and Remote Sensing in Environmental and Engineering Applications (Second Edition))

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27 pages, 22427 KB

Open AccessArticle

Multi-Camera Rig and Spherical Camera Assessment for Indoor Surveys in Complex Spaces

by Luca Perfetti, Nazarena Bruno and Riccardo Roncella

Remote Sens. 2024, 16(23), 4505; https://doi.org/10.3390/rs16234505 - 1 Dec 2024

Viewed by 2241

Abstract

This study compares the photogrammetric performance of three multi-camera systems—two spherical cameras (INSTA 360 Pro2 and MG1) and one multi-camera rig (ANT3D)—to evaluate their accuracy and precision in confined environments. These systems are particularly suited for indoor surveys, such as narrow spaces, where traditional methods face limitations. The instruments were tested for the survey of a narrow spiral staircase within Milan Cathedral and the results were analyzed based on different processing strategies, including different relative constraints between sensors, various calibration sets for distortion parameters, interior orientation (IO), and relative orientation (RO), as well as two different ground control solutions. This study also included a repeatability test. The findings showed that, with appropriate ground control, all systems achieved the target accuracy of 1 cm. In partially unconstrained scenarios, the drift errors ranged between 5 and 10 cm. Performance varied depending on the processing pipelines; however, the results suggest that imposing a multi-camera constraint between sensors and estimating both IO and RO parameters during the Bundle Block Adjustment yields the best outcomes. In less stable environments, it might be preferable to pre-calibrate and fix the IO parameters. Full article

(This article belongs to the Special Issue Photogrammetry and Remote Sensing in Environmental and Engineering Applications (Second Edition))

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