Publications

Book Chapters

E. Hoppe, B. Bruckno, E. Campbell, S. Acton, A. Vaccari, M. Stuecheli, A. Bohane, G. Falorni, and J. Morgan, “Transportation Infrastructure Monitoring Using Satellite Remote Sensing,” in Materials and Infrastructures 1, J.-M. Torrenti and F. La Torre, Eds. Hoboken, NJ: John Wiley & Sons, Inc., 2016, ch.14, pp.185-198. doi: 10.1002/9781119318583.ch14
The increased availability of civilian radar satellites, combined with the rapid progress in digital signal processing, renders InSAR technology attractive for long-term performance monitoring of transport infrastructure. This chapter presents the results of some practical applications of InSAR by the Virginia Department of Transportation (VDOT). The focus is primarily on transportation-related applications. Potential applications of InSAR technology as an alternate or complementary diagnostic tool for monitoring the transportation network are outlined. The objective of the study was to determine the feasibility of applying commercially available radar remote sensing technology to transportation network monitoring. Synthetic aperture radar data acquired from the Italian COSMO-SkyMed satellite were processed and analyzed. Specific applications included sinkhole detection in karst terrain, slope stability monitoring and infrastructure assessment. The study results indicate that satellite radar remote sensing can be effectively applied to performance monitoring of transportation infrastructure.

Journals

E. Hoppe, K. Young-Jun, B. Bruckno, S. Acton, L. Bolton, A. Becker, and A. Vaccari, "Historical Analysis of Tunnel Approach Displacements Using Satellite Remote Sensing," Transportation Research Record: Journal of the Transportation Research Board, Vol.2510, pp.15-23, 2015. doi: 10.3141/2510-03
This study investigated historical displacements of the tunnel boat sections at the approaches to the Monitor-Merrimac Memorial Bridge-Tunnel in Virginia as a potential reason for ongoing seawater infiltration. The analysis was based on archived data collected from December 2001 to March 2010 by the Radarsat-1 Earth orbiting radar satellite. Millimeter precision in displacement measurements was achieved over an area of approximately 100 km², including the bridge-tunnel and adjacent regions of Suffolk and Newport News. Data consisting of 42 radar acquisitions were processed using the differential technique of Interferometric Synthetic Aperture Radar (InSAR). Additional statistical analysis was conducted on the specific points of interest. The results of the historical analysis of satellite radar remote sensing data indicated no significant displacements of the tunnel boat sections during the period of study. The annual displacement rate precision of the tunnel boat sections was estimated to be within ±1 mm/year at the 95th percentile confidence interval. Thus, it is unlikely that the settlement of man-made islands was a reason for the ongoing water infiltration.

Vaccari, A.; Stuecheli, M.; Bruckno, B.; Hoppe, E.; Acton, S.T.; , "Detection of geophysical features in InSAR point cloud data sets using spatiotemporal models," International Journal of Remote Sensing, vol.34, no.22, pp.8215-8234. doi: 10.1080/01431161.2013.833357
In this article, we introduce an approach for detecting evolving geophysical features within interferometric synthetic aperture radar (InSAR)-derived point cloud data sets. This approach is based on the availability of models describing both spatial and temporal behaviours of the geophysical features of interest. The model parameters are used to generate a multidimensional space that is then scanned with a user-defined resolution. For each point in the parameter space, a spatiotemporal template is reconstructed from the original model. This template is then used to scan the point cloud data set for regions matching the spatiotemporal behaviour.
We also introduce a proportional measure where the residual for each point in the data set is compared to both the data and the template to provide a scale invariant measure of the behavioural matching. The matching is evaluated for every point in the parameter over a region of influence determined by the parameters. The resulting multidimensional space is then collapsed onto geographical coordinates to produce an overlay map identifying regions whose spatiotemporal behaviour matches the feature of interest.
We tailored our approach to the detection of subsidence behaviour, indicative of the development of sinkholes, modelled as Gaussian with amplitude linearly increasing with time. We verified the validity of our model using both synthetic and actual InSAR data sets. The latter was obtained by processing imagery of a region near Wink, Texas, containing ground truth sinkhole data.
We applied this framework to a 40 km x 40 km area of interest located in western Virginia and performed ground validation on a subset of the identified regions. The results show good agreement between the locations detected by our algorithm and the evidence of subsidence observed during the ground validation campaign.

This [.pdf] is an Accepted Manuscript of an article published in International Journal of Remote Sensing on 18 Sep 2013, available online: http://www.tandfonline.com/10.1080/01431161.2013.833357.

Dissertations and thesis

A. Vaccari, "An Automated Image Analysis Framework for Model-Based Feature Detection in Sparse Data," Ph.D. dissertation, Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 2014.
The development of novel remote sensing techniques, based on interferometric synthetic aperture RADAR (InSAR), currently allows for millimetric precision measurements of Earth surface deformation over time. One of the major challenges posed by these techniques, known as persistent scatterer interferometry (PSI), is the inherent sparsity of the data resulting from the RADAR scatterer selection process.

In this work we present an automated image analysis framework aimed at the detection of model-defined spatiotemporal features within sparse point cloud datasets, and we show how this framework can be tailored to the early detection of hazardous geophysical features within InSAR-derived data. In particular, we developed a spatiotemporal model describing the evolution of subsiding features, we verified its validity by using discrete element method simulations, and applied it, within our framework, to the early detection of sinkholes.

The ground truth dataset, used to develop the spatiotemporal model, was obtained by imaging a sinkhole prone area for a period of 70months. The relevance of this dataset for our research is due to the fact that it contains four active features of which one (W1) collapsed before the data was taken, and one (W2) collapsed after the data was taken providing ground truth measurements.

We first approached the detection as a graph segmentation problem. By assigning each PSI scatterer within the ground truth dataset to a vertex and enforcing connectivity by the Delaunay triangulation, we obtained a graph that reflected the local neighborhood relationship. We then constructed an edge-weighting function designed to favor low weights for edges traversing boundaries of regions displaying signs of subsidence. The segmentation resulting from the application of the min-cut algorithm to this graph captured 27% and 94% of the collapsed area of W1 and W2 respectively.

Since we had at our disposal the time series of the displacements of each scatterer, we expanded our approach to leverage this information by developing a model-based spatiotemporal detection method. The parameters regulating the behavior of the model were used to generate a multidimensional parameter space that was then scanned with user-defined resolution. At each point, a spatiotemporal template was reconstructed based on the original model and the currently selected parameters. This template was used to analyze the point cloud dataset for regions with matching behavior. This method provided an improvement by identifying as high risk 52.6% and 81.6% of the collapsed area of W1 and W2 respectively against the values of 37.5% and 17.6% obtained from the graph cut approach. We also applied this method to a 40km x 40km area of interest located in western Virginia. The ground validation on a subset of the detected features showed that 78% of the locations presented strong evidence of subsidence.

To improve on the computational burden imposed by the direct application of this exploration method with complex models over large datasets, we developed an activity detection approach where large datasets were subdivided into smaller blocks. The average and standard deviation of the displacements of the scatterers contained in each block were used as elements of each block feature vector. Outliers in the feature space, corresponding to actively subsiding regions, were identified using their Mahalanobis distance. When applied to the ground truth dataset this screening method provided a x15 increase in the detection speed while still maintaining accurate results. To further reduce the impact of larger datasets and complex models, we introduced a second screening stage, based on the evaluation of the normalized mutual information between model and data, to pinpoint the location of features requiring full spatiotemporal analysis.

Finally, to leverage the inherent sparsity of the PSI data, we took advantage of the tools provided by the emerging field of graph signal processing and developed a graph-based scale space analysis approach that provided results comparable to those obtained by previous methods.

Available online.

M. Stuecheli, "Automatic Sinkhole Detection from Satellite-based InSAR," M.S. thesis, Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 2013.
Recent developments in both the quality and processing of Interferometric Synthetic Aperture Radar (InSAR) data have allowed for remote analysis of ground deformation with unprecedented accuracy. SAR images acquired via the COSMO-SkyMed satellite constellation provide spatial resolutions on the order of a couple of meters per pixel; with a reasonable number of these images, SAR-processing algorithms known as PSInSAR and SqueeSAR can yield ground deformation information with millimeter accuracy. In this thesis, I present two novel approaches which utilize the aforementioned ground displacement measurements in order to detect and monitor geological hazards known colloquially as sinkholes. The first algorithm employs a graph theoretic approach to accomplish this goal, while the second utilizes a spatiotemporal parametric matching approach; both of these methods demonstrate strong efficacy in locating sinkholes from satellite-based InSAR-derived data.
Available online.

Conferences

B. Bruckno, E. Hoppe, A. Vaccari, S. Acton, and E. Campbell, "Integration and delivery of interferometric synthetic aperture radar [InSAR] data into stormwater planning within karst terranes," In Proceedings of the 14th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst, October 5-9, 2015, Rochester, Minnesota (Accepted for Publication)
As part of two USDOT-funded studies focused on the development of satellite-based Interferometric Synthetic Aperture Radar (InSAR) technology, the researchers integrated InSAR-derived point cloud data into the transportation design process to optimize the location of a stormwater management system in a karst terrane. After initial validation, the InSAR data (over 1.67 million data points comprising various “scatterers”) were brought into a GIS dataframe and georeferenced to locations of known sinkholes. This dataset was then used to evaluate karst hazard within a 40x40km data frame located in the Valley and Ridge Province of Virginia. The group identified systematic kinematic differences in scatterer behavior with respect to their proximity to mapped karst geohazards, and used this method to identify unknown karst features, revealing numerous previously unidentified sinkholes. After validating the data with quantitative field correlations, the group integrated the dataset into a traditional CADD-developed design, ported into a GIS environment, and utilized the resulting integrated dataset to optimize the location of stormwater management assets within a traditionally-developed roadway project. In the process, the group developed open-source data delivery, allowing greater flexibility, efficiency, and optimization of the infrastructure design and planning process conducted collaboratively over geospatial platforms. This data integration offers lifecycle cost benefits, improvements to the safety of the traveling public, and protection of the environment, particularly in groundwater-sensitive karst terranes. A case study of this approach is presented.

E. Hoppe, K. Young-Jun, B. Bruckno, S. Acton, L. Bolton, A. Becker, and A. Vaccari, "Historical Analysis of Tunnel Approach Displacements Using Satellite Remote Sensing (15-4928)," TRB 94th Annual Meeting Compendium of Papers, Washington, D.C., January 11-15, 2015 [.pdf]
This study investigated historical displacements of the tunnel boat sections at the approaches to the Monitor-Merrimac Memorial Bridge-Tunnel in Virginia as a potential reason for ongoing seawater infiltration. The analysis was based on archived data collected from December 2001 to March 2010 by the Radarsat-1 Earth orbiting radar satellite. Millimeter precision in displacement measurements was achieved over an area of approximately 100 km², including the bridge-tunnel and adjacent regions of Suffolk and Newport News. Data consisting of 42 radar acquisitions were processed using the differential technique of Interferometric Synthetic Aperture Radar (InSAR). Additional statistical analysis was conducted on the specific points of interest. The results of the historical analysis of satellite radar remote sensing data indicated no significant displacements of the tunnel boat sections during the period of study. The annual displacement rate precision of the tunnel boat sections was estimated to be within ±1 mm/year at the 95th percentile confidence interval. Thus, it is unlikely that the settlement of man-made islands was a reason for the ongoing water infiltration.

E. Hoppe, B. Bruckno, E. Campbell, S. Acton, A. Vaccari, M. Stuecheli, A. Bohane, G. Falorni, and J. Morgan, "Transportation infrastructure monitoring using satellite remote sensing," Proceedings of the Transportation Research Arena 2014, Paris - La Défense CNIT, France, 14-17 April 2014 [Proceedings]
The objective of this study was to determine the feasibility of applying commercially available radar remote sensing technology to transportation network monitoring. Synthetic aperture radar data acquired from the Italian COSMO-!SkyMed satellite were processed and analyzed. Specific applications included sinkhole detection in karst terrain, slope stability monitoring, and infrastructure assessment. A 40 x 40 km Area of Interest was identified in the proximity to the City of Staunton, in a region geologically prone to sinkhole formation. Satellite data from this area were acquired on a bi-monthly schedule, for a period of 14 months. Radar data were processed with the SqueeSAR algorithm. Additional software tools for automated sinkhole detection were developed. A new approach involving Temporary Coherent Scatterer (TS) was experimented with. Study results indicate that satellite radar remote sensing can be effectively applied to performance monitoring of transportation infrastructure.

B. S. Bruckno, A. Vaccari, E. Hoppe, W. Niemann, and E. Campbell, “Validation of Interferometric Synthetic Aperture Radar as a Tool for Identification of Geohazards and At-Risk Transportation Infrastructure,” Proceedings of the 64th Highway Geology Symposium, North Conway, New Hampshire, p. 100, Sept. 9-12, 2013 [Proceedings]
As part of the USDOT-funded research program RITA-RS-11-H-UVA, "Sinkhole Detection and Bridge/Landslide Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar InSAR Images," the authors broadly validated the use of InSAR data as a tool for early detection of geological hazards and failing infrastructure, including sinkhole development, potentially dangerous rock slopes, distressed bridges, rock buttresses, and other geotechnical assets. By bringing the InSAR dataset into a GIS dataframe and correlating the data to published maps of sinkhole locations and karst terranes, the authors were able to correlate average displacement velocities of InSAR data points (scatterers) with respect to their proximity to mapped sinkholes. Additionally, the authors correlated the InSAR signal characteristics with kinematic analysis of rock slopes using point-cloud data generated using digital photogrammetry and LiDAR. Lastly, the displacement time-series of the InSAR scatterers were used to screen for compromised geotechnical assets and infrastructure, and the findings were strongly confirmed by field inspection of distressed bridges and a failing rock buttress. The validation of InSAR data for these purposes thus allows generation of GIS-based geohazard and at-risk infrastructure/asset maps and provides the opportunity to augment or eventually replace a periodic inspection-based infrastructure management system with continuous performance-based system.

Vaccari, A.; Acton, S.T.; , "Spatiotemporal Gaussian feature detection in sparsely sampled data with application to InSAR," Proc. SPIE 8746, Algorithms for Synthetic Aperture Radar Imagery XX, 87460U (June 3, 2013) doi: 10.1117/12.2020669
Point cloud data present a broad swath of intriguing problems in signal processing. Namely, the data may be sparse, may be non-uniformly sampled in space and time, and cannot be processed directly by way of conventional techniques such as convolutional filters. This paper addresses such data under the application umbrella of remote sensing. Specifically, we examine the potential of interferometric synthetic aperture radar for detecting geohazards that affect transportation. Using sparsely distributed coherent scatterers on the ground, our algorithms attempt to locate events in process such as sinkholes in the vicinity of highways. Theoretically, the problem boils down to the detection of Gaussian-shaped changes that evolve predictably in space and time. The solution to the detection problem involves two basic approaches, one grounded in pattern matching and the other in statistical signal processing. Essentially, the spatiotemporal pattern matching extends a Hough-like voting algorithm to a method that penalizes deviation from the known model in space and time. For confirmation of geohazard location, we can exploit a fixed-time analysis of the distribution of subsidence from the point cloud data by way of computing mutual information. Results show that the detection and screening strategies conform to geological evidence.

Stuecheli, M.; Vaccari, A.; Acton, S.T.; , "Graph cut segmentation of sparsely sampled images with application to InSAR-measured changes in elevation," Image Analysis and Interpretation (SSIAI), 2012 IEEE Southwest Symposium on, vol., no., pp.149-152, 22-24 April 2012. doi: 10.1109/SSIAI.2012.6202475
In this paper, we outline an algorithm for the automatic segmentation of sparse data in order to detect possible terrain-deformation phenomena. Segmentation is accomplished through a graph cut technique. In the graph structure, for each edge, we derive a unique energy by combining multiple independent energies tailored toward accurately locating the boundaries of spot-like, subsiding regions. We then find the series of cuts with minimum total energy and fit splines to these cuts for smooth segment boundaries. The segmentation approach is applied to the problem of localizing sinkholes in karst regions. Test results indicate efficacy for a sufficient density of InSAR features.

Abstracts and presentations

B. Bruckno, E. Hoppe, A. Vaccari, S. Acton, and E. Campbell, "Integration of Interferometric Synthetic Aperture Radar Data into Geotechnical Planning and Design (P15-5128)," In Transportation Research Board 94th Annual  Meeting Final Program, Washington, D.C., January 11-15, 2015

B. Bruckno, E. Hoppe, A. Vaccari, S. Acton, E. Campbell, and W. Niemann, "New Applications for Interferometric Synthetic Aperture Radar InSAR: Interpretation of Scatterers for Rock Slope Evaluation," Virginia Geological Research Symposium, Charlottesville, Virginia, April 17, 2014

B. Bruckno, E. Hoppe, A. Vaccari, S. Acton, and E. Campbell, "New applications for interferometric synthetic aperture radar InSAR: interpretation of persistent, distributed, and temporary scatterers for geohazard and infrastructure monitoring and evaluation," Geological Society of America Abstracts with Programs, Vol. 46, No. 3, p. 22, April 10-11, 2014 [.ppsx] [GSA link]
As part of the USDOT-funded research program RITA-RS-11-H-UVA, “Sinkhole Detection and Bridge/Landslide Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar InSAR Images,” the authors validated new interpretations of InSAR data for early detection of geological hazards and incipient infrastructure failures, targeting sinkhole development, distressed bridges, and rock slope characterization. The authors acquired over one million InSAR data points (persistent, distributed, and temporary “scatterers”) within a 40 x 40 km Area of Interest in the Valley and Ridge Province of Virginia. By ingesting the various scatterers into a GIS dataframe and georeferencing their locations to published maps of sinkholes, locations of repaired sinkholes, and karst terranes, the authors were able to correlate kinematic differences in scatterer behavior with respect to their proximity to karst geohazards, and to identify unmapped sinkholes. Additionally, the authors used displacement time-series developed from the dataset to screen for compromised geotechnical assets and infrastructure. The field inspection of previously-unidentified distressed bridges and other deteriorating assets yielded a positive correlation, strongly validating the methodology implemented. Lastly, the authors correlated the InSAR data characteristics with kinematic analyses of rock slopes using point-clouds generated by digital photogrammetry and LiDAR, and correlated the data to qualitative rock slope behavior. The addition of temporary scatterers (a novel data analysis technique) as support to the data obtained by the established persistent and distributed scatterers greatly enriched the value of the InSAR dataset as a whole.

B. Bruckno, E. Hoppe, A. Vaccari, S. Acton, and E. Campbell, "New applications for interferometric synthetic aperture radar InSAR: Field validation studies of persistent, distributed, and temporary scatterers," Geological Society of America Abstracts with Programs, Vol. 46, No. 2, p. 92, March 23-25, 2014 [.ppsx] [GSA link]
As part of the USDOT-funded research program RITA-RS-11-H-UVA, “Sinkhole Detection and Bridge/Landslide Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar InSAR Images,” the authors conducted a validation study of new interpretations of InSAR data for early detection of geohazards and infrastructure failures, targeting sinkhole development, geotechnical assets, and rock slope hazard. The authors acquired over one million InSAR data points (persistent, distributed, and temporary “scatterers”) within a 40x40 km dataframe in the Valley and Ridge of Virginia. The authors then developed a quantitative field-verification method keyed to asset distress and geomorphological observations, ingested the various scatterers into a GIS dataframe, and georeferenced the data to locations of sinkholes, bridges, fills, pipes, and geotechnical assets. The authors identified kinematic differences in scatterer behavior with respect to their proximity to mapped karst geohazards, and used this method to identify previously-unmapped sinkholes. The authors then used displacement time-series of scatterers to screen for patterns suggesting compromised geotechnical assets and deteriorating infrastructure. In the case of both karst geohazards and infrastructure distress, the field validation yielded robust verification. Lastly, the authors correlated the InSAR data with kinematic analysis of rock slopes using point-cloud data generated by digital photogrammetry and LiDAR, and correlated the data to rock slope behavior. The novel use of temporary scatterers, and their interpretation in the light of persistent and distributed scatterers, greatly increased the utility of the InSAR dataset as a whole.

A. Vaccari, B. Bruckno, E. Hoppe, S. Acton, and E. Campbell, "Delivering geohazard and geotechnical data: From the satellite to the field," Geological Society of America Abstracts with Programs, Vol. 46, No. 2, p. 74, March 23-25, 2014 [.ppsx] [GSA link]
As part of the USDOT-funded research program RITA-RS-11-H-UVA, “Sinkhole Detection and Bridge/Landslide Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar InSAR Images,” the authors completed the a pilot study in which they developed a computational approach aimed at the early detection and evaluation of potential geohazards within a point cloud dataset obtained from processed InSAR data. The technique was applied to the detection of sinkholes within an active 40x40 km data frame located in the Valley and Ridge Province in Virginia.
The analysis, based on the detection of a specific spatio-temporal model describing incipient sinkhole behavior, was used to scan a 10 million point dataset for regions where the spatio-temporal behavior matched the model, providing as output a geo-referenced map indicating the quality of match. This map was then converted to a risk map where fastest growing features were identified as riskier. To favor visualization and integration with commonly used GIS platform, results were exported in KML (Google Earth) and SHP (ArcGIS) formats.
The authors believe this approach can be implemented as a map-production workflow where routine monitoring of satellite data is pushed within a GIS-integrated analysis pipeline to be analyzed by a set of plugins designed to monitor/detect potentially hazardous features, and the results exported as Google Earth (KML) files or ArcGIS layers to provide immediate visualization and delivery. Other geospatial data layers, such as geology, karst, soils maps, or fault zones, can be delivered on the same platforms, thus offering greater efficiency and geospatial data integration in planning, inspection, and incident response.

B. Bruckno, E. Hoppe, A. Vaccari, and E. Campbell, "Validation of new applications for interferometric synthetic aperture radar InSAR data: Geohazards and infrastructure distress," Geological Society of America Abstracts with Programs, Vol. 45, No. 7, p. 719, October 27-30, 2013 [.pps] [GSA link]
As part of the USDOT-funded research program RITA-RS-11-H-UVA, “Sinkhole Detection and Bridge/Landslide Monitoring for Transportation Infrastructure by Automated Analysis of Interferometric Synthetic Aperture Radar [InSAR] Images,” the authors validated new applications of InSAR data as a tool for early detection of geological hazards and incipient infrastructure failures, including sinkhole development, potentially dangerous rock slopes, distressed bridges, rock buttresses, and other geotechnical assets. First, by bringing the InSAR dataset into a GIS dataframe and georeferencing to published maps of sinkhole locations, locations of repaired sinkholes, and karst terranes, the authors were able to detect differences in average displacement velocities of InSAR data points (“scatterers”) with respect to their proximity to karst geohazards. Second, the authors correlated the InSAR signal characteristics with kinematic analysis of rock slopes using point-cloud data generated using digital photogrammetry and LiDAR. Third, the authors used displacement time-series of various InSAR scatterers to screen for compromised geotechnical assets and deteriorating infrastructure, and the findings were strongly confirmed by field inspection of previously-unidentified distressed bridges and a failing rock buttress, with strongly positive correlation values. Lastly, the authors used points yielded by a new processing method, referred to as “temporary scatterers,” to reveal areas of sudden or variable motion, greatly expanding the nature and number of data points and adding yet greater value to InSAR data. The validation of InSAR data for these purposes thus allows generation of GIS-based geohazard and at-risk infrastructure/asset maps and provides the opportunity to augment or eventually replace a periodic field inspection-based infrastructure management system with continuous performance-based system. Initial cost-benefit analyses suggest that deployment of such a system would yield positive cost benefits if the system allowed early stabilization of only one geotechnical failure, rather than a repair subsequent to failure.

A. Vaccari, M. Stuecheli, S.T. Acton, and B.S. Bruckno, "Monitoring the transportation infrastructure with satellite-based interferometric synthetic aperture radar (InSAR)," 13th Annual Technical Joint Forum, Geohazards Impacting Transportation in Appalachia, Interstate Technical Group on Abandoned Underground Mines, Harrisonburg, VA, July 30 to August 1, 2013 [.ppsx] [MU link]
Can spaceborne Interferometric Synthetic Aperture Radar (InSAR) be used to detect and monitor ground features of interest to the transportation community? Can leading edge satellite-based interferometric products help provide a proactive rather than reactive approach to potentially hazardous phenomena such as sinkhole, landslides and bridge settlements?
Over the last two years we conducted a study sponsored by the U.S. Department of Transportation Research and Innovative Technology Administration with the goal of answering these questions in collaboration with the Virginia Center for Transportation Innovation Research using novel InSAR products provided by our commercial partner: TRE-Canada. As part of this study we developed a feature tracking algorithm designed to detect specific trends in the ground deformation monitored by TRE products capable of achieving sub-centimeter displacement resolution.
We applied our approach to detect ground subsidence potentially indicative of the development of sinkholes within a 40x40km area centered roughly on the locality of Middlebrook in Augusta County, Virginia. This area was chosen for its diversity in geological condition and for historical sinkhole activity. Ground validation of a subset (32) of the detections obtained running our detection algorithm show that 78% of the identified regions showed strong signs of subsidence with a false positive rate of 10%.

W. Niemann, "Lessons Learned from Application of Ground-based Digital Photogrammetry to Small-scale Movement on Unstable Rock Slopes: A Case Study from Virginia’s Valley & Ridge," 13th Annual Technical Joint Forum, Geohazards Impacting Transportation in Appalachia, Interstate Technical Group on Abandoned Underground Mines, Harrisonburg, VA, July 30 to August 1, 2013 [MU link]
Topic revision: r9 - 04 Nov 2017, AndreaVaccari
 
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