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Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (14 September 2024) | Viewed by 14320

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Special Issue Editors

Prof. Dr. Bojan Stopar

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Guest Editor

Chair of Mathematical and Physical Geodesy and Navigation, Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: GNSS; static positioning; deformation monitoring; low-cost GNSS receivers; geodynamics

Prof. Dr. Krištof Oštir

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Guest Editor

Chair of Geoinformatics and Real Estate Cadastres, Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
Interests: satellite images time series; machine learning in earth observation; image processing; InSAR
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Special Issue Information

Dear Colleagues,

The use of Global Navigation Satellite Systems (GNSS), Earth observations from space, technologies and sensors such as Aerial and Terrestrial Laser Scanners (TLS, ALS), Structure from Motion (SfM) Photogrammetry, Inertial Measurement Units (IMU), and Total Positioning Systems (TPS) has caused a revolution in geodesy, surveying, and mapping. We are experiencing a new level as technology becomes accessible to almost everyone. The advent of low-cost sensors, smartphones, and apps enables the collection and processing of multi-sensor data that can be used in various applications with minimal effort and cost. The effect can be seen in the massive georeferenced data acquisition in near real-time from multiple platforms. Earth observation data, including satellite, aerial, and terrestrial imagery, is another source of spatial data that enables the creation of accurate maps and models of the Earth's surface. The technologies and data available today allow the development of new sophisticated and reliable applications for a wide range of users. Still, there are difficulties in GNSS data collection in obstructed areas and, more recently, attacks on the signals through jamming and spoofing. Terrestrial measuring systems require quality reference data (horizontal and vertical) to utilize their high-quality relative positioning capabilities. Challenges using mobile mapping are various and rise from ensuring the accuracy of data collected from different sensors, integrating data from multiple sources into a cohesive map or model to the privacy and security of data collected during mobile mapping operations. Difficulties in Earth observation data and technologies include the sheer amount of data generated by Earth observation satellites and other sources and its storage, processing, and analysis. The big challenge is combining data from multiple sources and scales, such as integrating satellite data with ground-based measurements. In this context, expertise in recent advances in GNSS positioning, multi-sensor data integration, Earth observation data processing, interpretation, and understanding is needed for the geodesy, surveying, and mapping industries to benefit from the development of positioning technologies, methods, and services.

The main aim of this Special Issue is to highlight the latest developments in algorithms, methods, and integration of positioning technologies/sensors (GNSS, IMU, TPS), recent advances in mass data collection, processing and integration (ALS, TLS, cameras, Mobile Mapping Systems), integration of georeferenced spatial models and Earth observation data for various geodetic, surveying, and mapping applications.

We encourage the submission of theoretical and applied research (review articles and technical notes are welcome). The potential topics include, but are not limited to, the following:

Geodetic, Surveying, and Mapping Concepts and Applications;
Technologies and Sensors for Terrestrial, Aerial, and Maritime Positioning (GNSS, IMU, TPS);
Data collection and processing of remotely sensed spatial data (TLS, ALS, satellite, aerial, and terrestrial imagery, Mobile Mapping Systems);
Hybridization of GNSS, IMU, TPS, TLS, ALS, Earth observation data;
Fusion of georeferenced spatial models and Earth observation data;
Integration of GNSS and InSAR processing;
Time series processing and application.

Prof. Dr. Bojan Stopar
Prof. Dr. Krištof Oštir
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. 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.

Keywords

geodesy, surveying, mapping
GNSS positioning, data processing, interference
aerial, terrestrial, and maritime positioning and applications
multi-sensor fusion
integration of terrestrial, GNSS, and earth observation data
InSAR, PS InSAR, SBAS InSAR
time series integration and analysis

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Published Papers (7 papers)

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Research

21 pages, 6225 KiB

Open AccessArticle

3D Surface Velocity Field Inferred from SAR Interferometry: Cerro Prieto Step-Over, Mexico, Case Study

by Ignacio F. Garcia-Meza, J. Alejandro González-Ortega, Olga Sarychikhina, Eric J. Fielding and Sergey Samsonov

Remote Sens. 2024, 16(20), 3788; https://doi.org/10.3390/rs16203788 - 12 Oct 2024

Viewed by 1406

Abstract

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF’s annual vapor production. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping)

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21 pages, 3978 KiB

Open AccessArticle

Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland

by Łukasz Janowski and Radosław Wróblewski

Remote Sens. 2024, 16(14), 2638; https://doi.org/10.3390/rs16142638 - 18 Jul 2024

Cited by 1 | Viewed by 1240

Abstract

The digital representation of seafloor, a challenge in UNESCO’s Ocean Decade initiative, is essential for sustainable development support and marine environment protection, aligning with the United Nations’ 2030 program goals. Accuracy in seafloor representation can be achieved through remote sensing measurements, including acoustic and laser sources. Ground truth information integration facilitates comprehensive seafloor assessment. The current seafloor mapping paradigm benefits from the object-based image analysis (OBIA) approach, managing high-resolution remote sensing measurements effectively. A critical OBIA step is the segmentation process, with various algorithms available. Recent artificial intelligence advancements have led to AI-powered segmentation algorithms development, like the Segment Anything Model (SAM) by META AI. This paper presents the SAM approach’s first evaluation for seafloor mapping. The benchmark remote sensing dataset refers to Puck Lagoon, Poland and includes measurements from various sources, primarily multibeam echosounders, bathymetric lidar, airborne photogrammetry, and satellite imagery. The SAM algorithm’s performance was evaluated on an affordable workstation equipped with an NVIDIA GPU, enabling CUDA architecture utilization. The growing popularity and demand for AI-based services predict their widespread application in future underwater remote sensing studies, regardless of the measurement technology used (acoustic, laser, or imagery). Applying SAM in Puck Lagoon seafloor mapping may benefit other seafloor mapping studies intending to employ AI technology. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping)

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19 pages, 12167 KiB

Open AccessArticle

3D Landslide Monitoring in High Spatial Resolution by Feature Tracking and Histogram Analyses Using Laser Scanners

by Kourosh Hosseini, Leonhard Reindl, Lukas Raffl, Wolfgang Wiedemann and Christoph Holst

Remote Sens. 2024, 16(1), 138; https://doi.org/10.3390/rs16010138 - 28 Dec 2023

Cited by 1 | Viewed by 1529

Abstract

Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping)

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25 pages, 8989 KiB

Open AccessArticle

Thermal Mapping from Point Clouds to 3D Building Model Facades

by Manoj Kumar Biswanath, Ludwig Hoegner and Uwe Stilla

Remote Sens. 2023, 15(19), 4830; https://doi.org/10.3390/rs15194830 - 5 Oct 2023

Cited by 6 | Viewed by 2567

Abstract

Thermal inspection of buildings regarding efficient energy use is an increasing need in today’s energy-demanding world. This paper proposes a framework for mapping temperature attributes from thermal point clouds onto building facades. The goal is to generate thermal textures for three-dimensional (3D) analysis. Classical texture generation methods project facade images directly onto a 3D building model. Due to the limited level of detail of these models, projection errors occur. Therefore, we use point clouds from mobile laser scanning extended by intensities extracted from thermal infrared (TIR) image sequences. We are not using 3D reconstructed point clouds because of the limited geometric density and accuracy of TIR images, which can lead to poor 3D reconstruction. We project these thermal point clouds onto facades using a mapping algorithm. The algorithm uses a nearest neighbor search to find an optimal nearest point with three different approaches: “Minimize angle to normal”, “Minimize perpendicular distance to normal”, and “Minimize only distance”. Instead of interpolation, nearest neighbor is used because it retains the original temperature values. The thermal intensities of the optimal nearest points are weighted by resolution layers and mapped to the facade. The approach “Minimize perpendicular distance to normal” yields the finest texture resolution at a reasonable processing time. The accuracy of the generated texture is evaluated based on estimating the shift of the window corner points from a ground truth texture. A performance metric root-mean-square deviation (RMSD) value that measures this shift is calculated. In terms of accuracy, the nearest neighbor method outperformed bilinear interpolation and an existing TIR image-based texturing method. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping)

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25 pages, 19658 KiB

Open AccessArticle

Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from a Mobile Mapping System Point Cloud Using Oriented Bounding Box Collision-Detection Techniques

by Seokchan Kang, Jeongwon Lee and Jiyeong Lee

Remote Sens. 2023, 15(19), 4656; https://doi.org/10.3390/rs15194656 - 22 Sep 2023

Viewed by 1775

Abstract

Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extraction usually requires labor-intensive manual work, and the automatic extraction, which can be applied universally for diverse curved road types, presents a challenge. Given this context, this study proposes a method to automatically extract road boundaries and linear road markings by applying an oriented bounding box (OBB) collision-detection algorithm. The OBBs are generated from a reference line using the point cloud data’s position and intensity values. By applying the OBB collision-detection algorithm, road boundaries and linear road markings can be extracted efficiently and accurately in straight and curved roads by adjusting search length and width to detect OBB collision. This study assesses horizontal position accuracy using automatically extracted and manually digitized data to verify this method. The resulting RMSE for extracted road boundaries is +4.8 cm and +5.3 cm for linear road markings, indicating that high-accuracy road boundary and road marking extraction was possible. Therefore, our results demonstrate that the automatic extraction adjusting OBB detection parameters and integrating the OBB collision-detection algorithm enables efficient and precise extraction of road boundaries and linear road markings in various curving types of roads. Finally, this enhances its practicality and simplifies the implementation of the extraction process. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping)

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17 pages, 7227 KiB

Open AccessArticle

A Method for Measuring Gravitational Potential of Satellite’s Orbit Using Frequency Signal Transfer Technique between Satellites

by Ziyu Shen, Wenbin Shen, Xinyu Xu, Shuangxi Zhang, Tengxu Zhang, Lin He, Zhan Cai, Si Xiong and Lingxuan Wang

Remote Sens. 2023, 15(14), 3514; https://doi.org/10.3390/rs15143514 - 12 Jul 2023

Cited by 1 | Viewed by 1730

Abstract

We introduce an approach for the direct measurement of the gravitational potential (GP) along the trajectory of a satellite, with a specific focus on Low-Earth Orbit (LEO) satellites. A LEO satellite communicates with several Geosynchronous Equatorial Orbit (GEO) satellites via frequency signal links. The GP difference can be measured in real-time using the gravitational frequency shift approach by equipping both LEO and GEO satellites with precise atomic clocks. Since the GP at the high orbits of the GEO satellites can be precisely determined by the present gravitational field model EGM2008, the GP along the LEO satellite’s trajectory can be determined. In this study, simulation experiments were conducted, featuring a GRACE-type satellite as the LEO satellite in communication with three equidistant GEO satellites. The results indicated that the accuracy of the GP measurements along the LEO satellite’s trajectory primarily depends on the precision of the onboard atomic clocks. Supposing optical atomic clocks attain an instability level of

1 \times 10^{- 17} τ^{- 1 / 2}

(

τ

in seconds), we determined the GP distribution covered by the LEO satellite’s trajectories with 30-day observations. Then, we determined a gravitational field at the centimeter level based on the GP distribution. The GP data derived from the trajectory of a LEO satellite can be utilized to establish temporal gravitational fields, which have broad applications in different disciplines. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping)

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22 pages, 9207 KiB

Open AccessArticle

A Cost-Effective GNSS Solution for Continuous Monitoring of Landslides

by Veton Hamza, Bojan Stopar, Oskar Sterle and Polona Pavlovčič-Prešeren

Remote Sens. 2023, 15(9), 2287; https://doi.org/10.3390/rs15092287 - 26 Apr 2023

Cited by 11 | Viewed by 2793

Abstract

The development of low-cost dual-frequency global navigation satellite system (GNSS) receivers in recent years has enabled the use of these devices in numerous applications. In the monitoring of natural hazards, such as landslides, these devices can be considered suitable sensors. In this work, dual-frequency GNSS receivers and antennas were used for setting up near-real-time continuous low-cost GNSS monitoring systems (LGMSs) under field conditions. The SimpleRTK2B board, which integrates the u-blox ZED-F9P dual-frequency GNSS chip and the survey-calibrated GNSS antenna are the main components of the GNSS system. The LGMS was installed and tested for six months in the Laze landslide located in the northwestern part of Slovenia. A total of four GNSS systems were deployed, three of which were located in pillars in the landslide itself and one in a stable area. Open-source software was used to postprocess the acquired data, providing daily coordinates in static relative and precise point positioning (PPP) positioning modes. The results of six months of near-real-time monitoring showed that the Laze landslide was stable during this period, with only minor changes in the vertical component. The trend of decreasing ellipsoid height was evident at all stations, although it was in the range of a few millimeters. To validate the results in static relative positioning mode, the coordinate differences between low-cost and high-end geodetic GNSS instruments were estimated and found to be in the range of 5 mm or less, while the difference between horizontal and spatial positions was less than 7 mm for all stations. The same data were processed in PPP, vertical displacements were not detected as in the static relative positioning mode due to the lower accuracy of the method itself. Considering the six-month performance of a low-cost GNSS system under field conditions, it can be emphasized that these devices are capable of performing near real-time continuous monitoring of slow movements with high accuracy and decreased costs. In addition, an experimental test was performed to identify the size of detected displacements in real-time kinematic (RTK). Based on the achieved results, it was concluded that 20 mm spatial displacements are detectable with LGMSs in RTK considering only 15 s of observations. Full article

(This article belongs to the Special Issue Advanced Remote Sensing Technology in Geodesy, Surveying and Mapping)

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