Special Issue "Geospatial Monitoring with Hyperspatial Point Clouds"

Special Issue Editors

Assoc. Prof. Michael James Olsen
E-Mail Website
Guest Editor
School of Civil and Construction Engineering, Oregon State University, Corvallis, United States
Tel. 5417379327
Interests: terrestrial laser scanning; geohazard mapping and monitoring; point cloud processing algorithms; terrain modeling; structure-from-motion photogrammetry; digital infrastructure asset management;
Assoc. Prof. Michael Starek
E-Mail Website
Guest Editor
Texas AandM University-Corpus Christi, School of Engineering and Computing Sciences, Corpus Christi, United States
Tel. 3618253978
Interests: structure-from-motion photogrammetry; lidar systems and 3D data processing; unmanned aircraft systems; coastal geomatics; machine learning
Assoc. Prof. Craig Glennie
E-Mail Website
Guest Editor
University of Houston, Department of Civil and Environmental Engineering, Houston, United States
Interests: kinematic remote sensing system integration and calibration; LiDAR processing and analysis; 3D change detection; open source software development
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Special Issue Information

Dear Colleagues,

In recent years, the capabilities and applications of advanced geospatial technologies—such as 3D laser scanning (i.e., lidar), structure from motion, multi-view stereo, photogrammetry, etc.—to support the spatio-temporal monitoring of the natural and built environment have exploded. These systems have become more portable, flexible, faster, and produce hyperspatial (sub-meter) point cloud data of higher quality in terms of resolution and precision. Example applications include geohazards (e.g., landslides, rockfall, seismic/tectonics, glacial degradation, and coastal erosion), ecosystems and biodiversity (e.g., forest biomass, post-fire regrowth, and habitat), infrastructure condition monitoring, and structural heath monitoring.   

The assortment of sensors used for these purposes have diverse specifications for range, resolution, and accuracy. While these systems provide data of high quality in terms of measurement precision and resolution, there are many challenges if applying these systems to monitoring applications. First, point density and occlusions can vary substantially across the datasets, resulting in difficulties applying processing workflows developed for other remote sensing technologies that produce more uniform and consistent datasets. Second, these systems rapidly produce immense amounts of data that often need to be aggressively downsampled in order to be utilized in the conventional analysis programs specific to many of the applications; this constrains the ability to detect small trends and subtle changes. Third, many analysis algorithms are not suited to handle the rich 3D geometric data provided by these sensors and often reduce the data to 2D, which can result in distortions. Further, a variety of workflows are used for different stages of data processing that can result in systematic biases in the data. Lastly, the data quality can vary substantially with the sensors utilized, and the georeferencing methods employed and monitoring results are highly dependent on rigorous geodetic control and procedures. These challenges significantly affect the ability to reliably use point clouds for monitoring applications. Further, the high processing burden can limit the timeliness and value of the monitoring information provided in point clouds.

Fortunately, many promising solutions are emerging through point cloud research in the various communities utilizing these sensors for monitoring. Another key opportunity and challenge lies in the versatility of the technologies being utilized by a wide range of communities for different monitoring applications. As a result, research developing techniques and validating them through case studies are scattered across these disciplines and often this information is redeveloped by other communities. However, this versatility presents a unique opportunity to synthesize and integrate these experiences and expertise across these disciplines as a broader geospatial community.

To this end, this Special Issue promotes new and innovative field procedures, data acquisition techniques, data processing and analysis algorithms to support monitoring, combined sensor or geospatial data integration, and uncertainty modelling for improved monitoring with point clouds. We invite submissions of either original technical papers or high-quality review papers that shed new light on a particular perspective of geospatial monitoring with point clouds. Contributions that develop techniques relevant to monitoring (e.g., point cloud classification) are welcome, but should provide a clear application to monitoring rather than presenting a generic approach. Likewise, monitoring applications that do not utilize a point cloud in some form will not be considered for the Special Issue. We encourage you to participate in this important Special Issue and hope to see your contribution!

Assoc. Prof. Michael James Olsen
Assoc. Prof. Michael Starek
Assoc. Prof. Craig Glennie
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 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

  • Hazard monitoring such as earthquakes/tectonics, hurricanes, landslides, coastal change, ecosystems, etc
  • Post-disaster reconnaissance and forensic investigations
  • Infrastructure condition assessment and monitoring
  • Ecosystem and habitat monitoring
  • Traffic monitoring
  • Autonomous systems for novel 3D monitoring applications
  • Structural health monitoring with geospatial technologies
  • Multi-sensor fusion (e.g., unmanned aircraft systems, UAS, SfM photogrammetry combined with terrestrial laser scanning)
  • Novel surveying and geodetic control procedures to support monitoring applications
  • Point cloud data to validate numerical/analytical modeling of deformation
  • Deformation/change detection and analysis algorithms and techniques
  • Uncertainty modeling, particularly in terms of the uncertainty present in the deformation analysis when comparing multiple epochs of data
  • 3D data processing and machine learning for point cloud classification relevant to monitoring

Published Papers (2 papers)

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Research

Open AccessArticle
Ensemble Neural Networks for Modeling DEM Error
ISPRS Int. J. Geo-Inf. 2019, 8(10), 444; https://doi.org/10.3390/ijgi8100444 - 09 Oct 2019
Abstract
Digital elevation models (DEMs) have become ubiquitous and remarkably effective in the field of earth sciences as a tool to characterize surface topography. All DEMs have a degree of inherent error and uncertainty that is propagated to subsequent models and analyses, which can [...] Read more.
Digital elevation models (DEMs) have become ubiquitous and remarkably effective in the field of earth sciences as a tool to characterize surface topography. All DEMs have a degree of inherent error and uncertainty that is propagated to subsequent models and analyses, which can lead to misinterpretation and inaccurate estimates. A new method was developed to estimate local DEM errors and implement corrections while quantifying the uncertainties of the implemented corrections. The method is based on the flexibility and ability to model complex problems with ensemble neural networks (ENNs). The method was developed to be applied to any DEM created from a corresponding set of elevation points (point cloud) and a set of ground truth measurements. The method was developed and tested using hyperspatial resolution terrestrial laser scanning (TLS) data (sub-centimeter point spacing) collected from a marsh site located along the southern portion of the Texas Gulf Coast, USA. ENNs improve the overall DEM accuracy in the study area by 68% for six model inputs and by 75% for 12 model inputs corresponding to root mean square errors (RMSEs) of 0.056 and 0.045 m, respectively. The 12-input model provides more accurate tolerance interval estimates, particularly for vegetated areas. The accuracy of the method is confirmed based on an independent data set. Although the method still underestimates the 95% tolerance interval, 8% below the 95% target, results show that it is able to quantify the spatial variability in uncertainties due to a relationship between vegetation/land cover and accuracy of the DEM for the study area. There are still opportunities and challenges in improving and confirming the applicability of this method for different study sites and data sets. Full article
(This article belongs to the Special Issue Geospatial Monitoring with Hyperspatial Point Clouds)
Open AccessArticle
UAV Photogrammetry-Based 3D Road Distress Detection
ISPRS Int. J. Geo-Inf. 2019, 8(9), 409; https://doi.org/10.3390/ijgi8090409 - 12 Sep 2019
Abstract
The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low [...] Read more.
The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice. Full article
(This article belongs to the Special Issue Geospatial Monitoring with Hyperspatial Point Clouds)
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