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Matthew P. Conlin

Publications and source records attributed to Matthew P. Conlin.

4 recordsLinked to original sources

On the potential for remote observations of coastal morphodynamics from surf-cameras

Recreational surf-cameras (surfcams) are ubiquitous along many coastlines, and yet are a largely untapped source of coastal morphodynamic observations. Surfcams offer broad spatial coverage and flexibility in data collection, but a method to remotely acquire ground control points (GCPs) and initial camera parameter approximations is necessary to better leverage this existing infrastructure to make quantitative measurements. This study examines the efficacy of remotely monitoring coastal morphodynamics from surfcams at two sites on the Atlantic coast of Florida, U.S.A., by leveraging freely available airborne lidar observations to acquire remote-GCPs and open-source web tools for camera parameter approximations, ignoring lens distortion. Intrinsic and extrinsic camera parameters are determined using a modified space resection procedure, wherein parameters are determined using iterative adjustment while fitting to remote-GCPs and initial camera parameter approximations derived from justified assumptions and Google Earth. This procedure is completed using the open-source Surf-Camera Remote Calibration Tool (SurfRCaT). The results indicate root mean squared horizontal reprojection errors at the two cameras of 3.43 m and 6.48 m. Only immobile hard structures such as piers, jetties, and boulders are suitable as remote-GCPs, and the spatial distribution of available points is a likely reason for the higher accuracy at one camera relative to the other. Additionally, lens distortion is not considered in this work. This is another important source of error and including it in the methodology is highlighted as a useful avenue for future work. Additional factors, such as initial camera parameter approximation accuracy, likely play a role as well. This work illustrates that, provided there is sufficient remote-GCP availability and small lens distortion, remote video monitoring of coastal areas with existing surfcams could provide a usable source of coastal morphodynamic observations. This is further explored with a shoreline change analysis from the higher-accuracy camera. It was found that only the largest (>6 m) magnitude shoreline changes exceed the observational uncertainty driven by shoreline mapping error and reprojection error, indicating that remotely calibrated surfcams can provide observations of seasonal or storm-driven signals.

Florida

Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.

Earth and Space Science

SurfRCaT: A tool for remote calibration of pre-existing coastal cameras to enable their use as quantitative coastal monitoring tools

The Surf-camera Remote Calibration Tool (SurfRCaT) is a Python-based software application to calibrate and rectify images from pre-existing video cameras that are operating at coastal sites in the United States. The software enables remote camera calibration and subsequent image rectification by facilitating the remote-extraction of ground control points using airborne lidar observations, and guides the user through the entire process. No programming or code interaction are necessary to use the software. Calibration parameters and subsequent rectified image products derived from the software are saved locally. Users can apply SurfRCaT to any camera imagery that has stationary structures within the camera’s field of view. Given current recreational camera infrastructure, SurfRCaT could increase the number of potential quantitative coastal video monitoring stations in the United States by an order of magnitude.

SoftwareX

Daily to decadal variability of beach morphology at NASA-Kennedy Space Center: Storm influences across timescales

Shoreline variability over timescales ranging from days to decades is examined at NASA-Kennedy Space Center on the Atlantic coast of Florida. Three sources of shoreline position data are utilized to complete this analysis: hourly video-image observations, monthly Real Time Kinematic GPS observations, and historical aerial imagery dating back to 1943. We find that shoreline positions tend to respond coherently to monthly variations in wave energy, except during storm-driven shoreline change. A seasonal signal in shoreline behavior is also evident, however this signal becomes indiscernible after a rapid change in morphologic behavior following Hurricane Sandy’s impact in October 2012. Further, the spatial pattern of shoreline change following Sandy’s impact mimics decadal-scale trends, suggesting that response to large storms may be controlling decadal shoreline change behavior at this site.

Florida