Dr. Mohammad Pashaei Publishes Journal in SALIS

Former MANTIS Ph.D. student and post-doctoral researcher recently published an article in the Journal of Surveying and Land Information Science (SALIS) titled “UAS-SfM Crash Scene Reconstruction in a Controlled Environment: Effects of Image Overlap and Camera Tilt.” The paper explores how key photographic flight parameters, specifically image overlap and camera tilt, affect the quality of 3D reconstructions of crash scenes using UAS-SfM workflows. The work was conducted in a controlled environment to simulate crash-scene conditions while allowing systematic variation of parameters.

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The abstract reads as follows:

The integration of uncrewed aircraft system (UAS) technology with structure-from-motion / multi-view stereo (SfM/MVS) photogrammetry enables efficient and high-resolution three-dimensional (3D) reconstruction of crash scenes. This process, commonly referred to as UAS-SfM, facilitates the generation of geospatial products such as dense point clouds, textured meshes, digital surface models, and orthophotos that support post-crash activities, including liability assessment and roadway clearance. However, the accuracy and reliability of these products depend on a range of factors, including sensor-based, environmental, and UAS mission parameters. Isolating the impact of individual variables on UAS reconstruction quality is difficult due to their complexity and interdependence. To address this challenge, this study leverages a controlled, simulated crash scene to systematically evaluate how changes in image overlap and camera tilt affect UAS-SfM reconstruction quality. Results suggest that image overlap below 70 percent reduces point cloud density, increases vertical error, and degrades camera calibration accuracy, particularly in the vertical dimension. On the other hand, moderate camera tilt angles (≤20°) improve the reconstruction of complex and vertical surfaces without significantly degrading accuracy. However, excessive tilt (>30°) may introduce increased reprojection error and positional uncertainty. These results offer practical guidance for optimizing UAS-SfM workflows in hazardous crash response environments, improving the reliability of spatial data products for transportation safety and legal investigations.

MANTIS Students Showcase Research at IGARSS 2025 in Brisbane, Australia

MANTIS students Benjamin Ghansah and José Pilartes-Congo recently attended and presented their research at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS) held in Brisbane, Australia, between August 3 and August 8. 2025.

Benjamin Ghansah, PhD student in Geospatial Computer Science, presented his research titled "Predicting Canopy Height of Crops Using ICESat-2 Photons, SkySat Images and ResUNet Deep Learning Model." His work demonstrated innovative integration of satellite and deep learning technologies for precision agriculture.

José Pilartes-Congo, also a PhD student in the Geospatial Computer Science program, presented his research titled “UAS Surveying of Wetlands: Comparing SfM and Lidar for 3D Reconstruction and DTM Generation," which compared the performance of specific UAS-SfM and UAS-Lidar sensors for wetland mapping. José also presented (on behalf of recent MANTIS graduate Sabin J. Pandey) a piece of research titled “Evaluation of SfM-MVS Apple Lidar Data for Coastal Monitoring."

The symposium provided valuable exposure and feedback, further enriching the students' academic trajectories.

Isabel A. Garcia-Williams Graduates

MANTIS Ph.D. student Isabel A. Garcia-Williams has recently graduated from the Coastal and Marine System Science doctoral program at TAMUCC. During her time with MANTIS, Dr. Garcia-Williams was engaged research involving mobile lidar deployment for monitoring coastal environments and surrounding habitats. Her work has led to several peer-reviewed journal/conference publications and conference presentations. Dr. Garcia-Williams went on to pursue a career in research and development in California.

Mohammad Sohail Presents Research at the 2025 CIG Community Workshop in Denver, Colorado

MANTIS and Computer Science PhD student Mohammad Sohail recently attended and presented his research at the 2025 Computational Infrastructure for Geodynamics (CIG) Community Workshop, held in Denver, Colorado, between August 3 and August 8, 2025. His research titled “Monitoring Flood-Induced Land Surface Deformation Using Sentinel SAR Interferometry and Optical Remote Sensing” explored the integration of advanced computer techniques and remote sensing to evaluate the impact of flood events on land surface characteristics.

Sabin J. Pandey Graduates

MANTIS master’s student Sabin J. Pandey has recently graduated from the Geospatial Systems Engineering program at TAMUCC. During his time with MANTIS, Sabin was engaged research involving close-range remote sensing techniques for monitoring campus facilities, particularly the use of UAS, lidar, and digital twins for as-built utility inventory. Sabin went on to work for an surveying firm in Florida as he continues to pursue a career as an RPLS. We wish him the very best in his future endeavors.

Sabin Pandey Defends Thesis

Congratulations to MANTIS and GSEN master student Sabin J. Pandey for successfully defending his dissertation, titled “Evaluating the Performance of Apple Pocket Lidar: Case Studies in Natural and Built Environments”.

As a GSEN student, Sabin worked under the advisement of Dr. Michael Starek. His research has focused on evaluating the capabilities and limitations of Apple pocket lidar (PL) sensor through case studies in both natural and built environments. Before his journey at MANTIS, Sabin received a B.E. in Geomatics Engineering from Purbanchal University in Nepal and worked as an academic for a brief period. Following graduation, he plans to relocate to Tampa, Florida, to join Element Engineering LLC and continue his career in the field of Geomatics. We wish him well in his future endeavors.

PRESENTATION ABSTRACT

Developments in light detection and ranging (lidar) systems have enabled integration of lidar sensors into consumer-grade Apple devices, opening new possibilities for low-cost, rapid, and accessible 3D data acquisition. This study evaluates the performance and practicality of the Apple pocket lidar (PL) sensor, using the iPad Pro with Pix4Dcatch and 3D Scanner apps, in both natural and built environments on the Texas A&M University–Corpus Christi (TAMUCC) campus. High-accuracy geomatics techniques, including real-time kinematic-global navigation satellite system (RTK-GNSS), terrestrial laser scanner (TLS), and total station (TS), were employed to acquire reference datasets. Uncrewed aircraft system (UAS)-based structure from motion (SfM) photogrammetry was utilized to explore data integration with PL-derived SfM/multi-view stereo (SfM-MVS) data. Lidar-only data from the 3D Scanner app produced smoother but less detailed surfaces, while photogrammetry-only scans offered richer texture but struggled in low-texture or homogenous areas, suggesting that a hybrid approach yields more complete data. Multiple scanning strategies (straight-line, loop, zigzag) were tested to examine data quality and algorithm behavior. Loop scans achieved the highest slope distance accuracy (2.7 cm root mean square error (RMSE)), with progressively decreasing errors in straight-line and loop scans, indicating the use of a simultaneous localization and mapping (SLAM)-like algorithm. Zigzag scans exhibited greater drift due to frequent directional changes.

 In coastal applications, PL-derived digital elevation models (DEMs) of University Beach, a restored beach on the TAMUCC campus, achieved a vertical RMSE of approximately 10 cm relative to TLS reference DEMs, though targeting larger areas led to surface inconsistencies. For utility mapping, PL captured dense, high-resolution point clouds, with cloud-to-cloud (C2C) differences within 1.4 cm of TLS data. Data from Pix4Dcatch, outperformed raw PL scans from the 3D Scanner app in slope and width measurements. The integration of PL and UAS-SfM data within Pix4Dmatic provided improved geometric alignment compared to manual co-registration methods. Overall, the results demonstrate that PL is a practical tool for high-resolution, small to medium-scale localized 3D mapping, with strong potential in data fusion workflows and digital twin initiatives.

Isabel Garcia-Williams Defends Dissertation

Congratulations to MANTIS and CMSS Ph.D. student Isabel Garcia-Williams for successfully defending her dissertation, titled “Evaluation and Application of Mapping-Grade Mobile Lidar Scanning (MLS) for Coastal Zone Monitoring” on June 26, 2025.

While at MANTIS, Isabel’s research focused on assessing and utilizing a mapping-grade Mobile Lidar Scanning (MLS) system for monitoring sandy beach coastal corridor environments. Before her doctoral journey, Isabel received a Bachelor’s degree in Surveying Engineering with a minor in Mathematics from New Mexico State University and a Master’s degree in Geospatial Surveying Engineering from Texas A&M University-Corpus Christi. We wish her well in her future endeavors.

PRESENTATION ABSTRACT

Vehicle-based mapping-grade mobile lidar scanning (MLS) systems collect high-resolution, three-dimensional point cloud data and allow for rapid deployment and flexible operation. They typically integrate a lidar scanner, mobile platform, position and orientation system (POS), camera, control system, and rigid mount. Unlike survey-grade MLS systems, which prioritize high precision, accuracy, and long-range scanning, mapping-grade systems generally integrate less capable lidar sensors and lower-grade POS components, resulting in relatively lower cost and a smaller form factor. These characteristics make mapping-grade MLS particularly useful for rapid deployment in coastal mapping and monitoring applications, where ease of use and mobility are important, and conditions are conducive to vehicle-based scanning. However, these benefits come with potential limitations, including reduced scanning range and lower positional accuracy and precision. Despite these limitations, mapping-grade MLS systems can provide accurate, detailed point cloud data of the beach and lower foredune, enabling the generation of high-resolution digital elevation models (DEMs) to support analysis of beach geomorphology, shoreline dynamics, sediment transport, coastal engineering projects, and post-storm impacts.

This study evaluates the application of a mapping-grade MLS system in sandy beach environments to support mapping and monitoring aimed at informing coastal management decisions. It is structured around three core objectives: (1) development of an optimized survey workflow for MLS system data collection and processing tailored to sandy beach corridors; (2) application of the workflow to assess shoreline position and geomorphic changes on a seawall-adjacent beach at North Padre Island, Texas, to guide bollard placement for vehicle access control and evaluate nourishment performance, including a comparative analysis of MLS and uncrewed aircraft system (UAS) photogrammetry for beach monitoring; (3) application of the workflow to assess seasonal vulnerability of Kemp’s ridley sea turtle (Lepidochelys kempii) nesting beaches along Padre Island National Seashore (PAIS) using an adapted Coastal Engineering Resiliency Index (CERI), supplemented by airborne lidar scanning (ALS) datasets for historical analysis. Overall, this work demonstrates the practical utility of mapping-grade MLS scanning in capturing coastal change and highlights its advantages, limitations, and potential for supporting coastal policy, resiliency planning, and resource management.

Isabel Garcia-Williams Publishes Article in the Journal of Coastal Research

MANTIS Ph.D. Candidate Isabel Garcia-Williams has recently published an article in the Journal of Coastal Research titled "Development of an Optimized Survey Workflow for Sandy Beaches with Mapping-Grade Mobile LIDAR”. The publication presents a refined approach to coastal data acquisition using mobile LiDAR systems, emphasizing efficiency, accuracy, and reproducibility for shoreline monitoring and geomorphic assessments.

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The abstract reads as follows:

Mapping-grade mobile LIDAR scanning (MLS) systems have increasing appeal for coastal surveying, because they are becoming more cost effective and compact in comparison to the more expensive, higher-caliber, survey-grade MLS systems. Despite the misconception that these systems are plug and play, they should be evaluated, and sources of error must be understood to generate consistent, accurate data. This study assesses a miniaturized, mapping-grade MLS system to develop an optimized, validated survey workflow for rapid coastal corridor mapping of sandy beaches. The MLS system, called the HiWay Mapper, integrates a Velodyne HDL-32E LIDAR scanner, a NovAtel inertial navigation system, and a FLIR Ladybug 360° spherical camera. A four-part framework is introduced, in which a series of rigorous experiments were conducted to evaluate and validate system performance to generate a repeatable workflow for collecting high-accuracy, three-dimensional point cloud data of sandy beaches and foredunes. The framework of (1) sensor characterization and setup, (2) quality assurance, (3) data processing and quality control, and (4) postprocessing will ultimately support the production of georeferenced digital elevation models (DEMs) to monitor geomorphology changes of sandy beach and foredune systems. The final workflow was evaluated on a 4-km stretch of sandy beach on Padre Island National Seashore, Texas. Two surveys were completed on 26 July 2022 and 22 September 2022 to provide examples of workflow repeatability and vertical root-mean-square error (RMSE) measures. The final DEM vertical RMSEs were 0.039 and 0.037 m, respectively. Cross-shore transects were also used to extract metrics to compute shoreline movement, beach width, dune slope, and beach slope to show seasonal dynamics. The experiments, results, and workflow presented herein, along with guidance, should benefit coastal researchers seeking to integrate mapping-grade MLS systems into their data collection workflow.

Dr. Tianxing Chu promoted to Associate Professor of Computer Science

The Texas A&M University System Board of Regents has announced the approval of Dr. Tianxing Chu’s promotion to Associate Professor of Computer Science. Promotion to the rank of Associate Professor is a recognition of the maturity and experience of a faculty member’s professional success as they increase their leadership within the academic profession. Dr. Chu is both a professor in TAMU-CC's College of Engineering and Computer Science and the Associate Director of CBI's Measurement Analytics (MANTIS) Lab. We congratulate Dr. Chu on his esteemed achievements!

TxDOT Highlights MANTIS Research

The Measurement Analytics (MANTIS) Lab at Texas A&M University-Corpus Christi was recently recognized by the Texas Department of Transportation (TxDOT) for its pioneering research on unmanned aircraft systems (UASs) for geospatial data acquisition. The research, funded and supported by TxDOT, was featured at the 2025 ASCE Texas Section Utility Engineering and Surveying Institute (TxUESI) Conference, underscoring the lab’s role in advancing innovation in transportation infrastructure through cutting-edge remote sensing technologies.

The study, formally titled Unmanned Aircraft Systems in Land Surveying: A Comparative Study of LiDAR and Photogrammetry, investigated the capabilities of UAS platforms equipped with digital cameras and light detection and ranging (LiDAR) sensors for surveying and mapping applications. The research focused on evaluating the accuracy, repeatability, and cost-effectiveness of UAS-based structure-from-motion / multi-view stereo (SfM/MVS) photogrammetry (or UAS-SfM) and UAS-LiDAR under varied field conditions. Comprehensive field campaigns were conducted across multiple geographic regions in Texas, assessing the performance of both technologies in diverse terrain types and environmental settings. These trials examined 3D data fidelity, explored different configurations of ground control, and analyzed data processing and post-processing workflows. The study also identified operational strengths and limitations inherent to each approach, providing actionable guidance for the integration of UAS technologies into transportation survey workflows.

During the conference presentation, Ronny Lackey (representing TxDOT) emphasized the study’s significance in supporting TxDOT’s digital delivery initiative. He highlighted the practical value the findings offer to surveyors and engineers statewide, particularly in enhancing the efficiency, safety, and quality of geospatial data collection for transportation projects.

The MANTIS Lab’s collaboration with TxDOT reflects a broader commitment to research-driven solutions that align with the evolving needs of infrastructure development. The full research report is available through the Lab Resources >> Technical Reports on this website.

MANTIS and CBI Support TxUESI Conference

The Measurement Analytics Lab (MANTIS), in collaboration with the Conrad Blucher Institute for Surveying and Science (CBI), played a central role in organizing and supporting the 2025 Texas UESI (TxUESI) Conference, held May 21–23 at Texas A&M University-Corpus Christi.

Hosted on the university's campus, this year’s TxUESI Conference brought together engineers, surveyors, researchers, students, and industry leaders from across Texas to explore emerging trends, technologies, and standards in utility engineering and subsurface investigations. The event featured technical sessions, equipment demonstrations, and professional networking opportunities.

MANTIS researchers and CBI staff were actively involved in the event’s coordination, including the development of session tracks on advanced surveying and sensing methods, digital twin applications, UAS-based data acquisition, and geospatial analytics. The conference also highlighted the critical role of academic-industry partnerships in advancing subsurface utility engineering practices.

The 2025 TxUESI Conference underscored the importance of interdisciplinary collaboration to address infrastructure challenges across Texas and beyond.

Dr. Bradley Koskowich Publishes Article on Exploring Monoplotting for Cross-View Geo-Localization

Former MANTIS student Dr. Bradley Koskowich has recently published an article in the ISPRS Open Journal of Photogrammetry and Remote Sensing titled “The Potential & Limitations of Monoplotting in Cross-View Geo-Localization Conditions.” This publication stems from Dr. Koskowich’s doctoral dissertation research during his time at MANTIS.

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The abstract reads as follows:

Cross-view geolocalization (CVGL) describes the general problem of determining a correlation between terrestrial and nadir oriented imagery. Classical keypoint matching methods find the extreme pose transitions between cameras present in a CVGL configuration challenging to operate in, while deep neural networks demonstrate superb capacity in this area. Traditional photogrammetry methods like structure-from-motion (SfM) or simultaneous localization and mapping (SLAM) can technically accomplish CVGL, but require a sufficiently dense collection of camera views in order to recover camera pose. This research proposes an alternative CVGL solution, a series of algorithmic operations which can completely automate the calculation of target camera pose via a less common photogrammetry method known as monoplotting, also called single camera resectioning. Monoplotting only requires three inputs, which are a target terrestrial camera image, a nadir-oriented image, and an underlying digital surface model. 2D-3D point correspondences are derived from the inputs to optimize for the target terrestrial camera pose. The proposed method applies affine keypointing, pixel color quantization, and keypoint neighbor triangulation to codify explicit relationships used to augment keypoint matching operations done in a CVGL context. These matching results are used to achieve better initial 2D-3D point correlations from monoplotting image pairs, resulting in lower error for single camera resectioning. To gauge the effectiveness of the proposed method, this proposed methodology is applied to urban, suburban, and natural environment datasets. This proposed methodology demonstrates an average 42x improvement in feature matching between CVGL image pairs, which improves on inconsistent baseline methodology by reducing translation errors between 50%–75%.

José Pilartes-Congo Acquires SIT Certification

MANTIS Ph.D. student José Pilartes-Congo recently acquired his Surveyor-in-Training (SIT) certification in the state of Texas after successfully passing the examination administered by the Texas Board of Professional Engineers and Land Surveyors (TBPELS). This certification marks an important step toward professional licensure and recognizes José’s technical expertise in geospatial and land surveying principles. MANTIS celebrates this accomplishment as a testament to José’s dedication and the group’s continued commitment to advancing excellence in geospatial engineering.

Mohammad Sohail Presents Research at 2025 NSF GAGE/SAGE Community Science Workshop

Congratulations to Mohammad Sohail, MANTIS student in the Computer Science Ph.D. program, who recently presented his research at the 2025 NSF GAGE/SAGE Community Science Workshop in Bloomington, Minnesota. The workshop focused on geophysical research exploring the solid Earth, cryosphere, oceans, atmosphere, and more. While there, Mohammad presented his research on land deformation and risk assessment related to the 2022 Southern Flood Plain in Pakistan event.

MANTIS Hosts Students During NOAA Workshop

The Measurement Analytics Lab (MANTIS) recently welcomed a group of students as part of a National Oceanic and Atmospheric Administration (NOAA) workshop aimed at advancing education and exposure in coastal and geosciences. The visit, held in conjunction with a broader initiative to showcase research facilities across the university, centered on the application of remote sensing technologies for surveying and mapping, with a special focus on coastal studies, change detection, and precision measurement and analytics.

Students engaged in discussions led by MANTIS researchers and observed demonstrations highlighting the lab’s cutting-edge capabilities in remote sensing data acquisition, processing, and analysis. Particular emphasis was placed on techniques for monitoring coastal environments, detecting environmental and anthropogenic changes over time, and ensuring accuracy in spatial data for scientific and operational use.

Dr. Michael J. Starek, Director of MANTIS, also served as a featured speaker during the workshop. In his address, he underscored the critical role of measurement science in contemporary geospatial research, particularly as coastal regions face increasing environmental pressures.

Ahmed Omar Presents Research at the 2025 TAMIDS Scientific Machine Learning (SciML) Summer School

Congratulations to Ahmed Omar, MANTIS student in the Coastal Marine Systems Science Ph.D. program, who recently presented his research and took part in the 2025 TAMIDS Scientific Machine Learning (SciML) Summer School. Held over five days, the program introduced a select group of students to the fundamentals of Physics-Informed Neural Networks (PINNs) and Scientific Machine Learning (SciML).

Nicholas Lincks Graduates

MANTIS undergraduate student Nicholas Lincks has recently graduated from the Geospatial Science program at TAMUCC. During his time with MANTIS, Nicholas was engaged in remote sensing applications for surveying and mapping and also served as a founding member and president of the ASPRS Student Chapter for Texas A&M University-Corpus Christi. Nicholas will go on to work for Dallas Aerial Mapping as he continues to pursue licensure as an RPLS. We wish him the very best in his future endeavors.

MANTIS Students Present at 2025 SSIRCA

MANTIS students Sabin Pandey, Ahmed Omar, and Mohammad Sohail presented their research at the 2005 Student Symposium for Innovation Research & Creative Activities (SSIRCA) held at Texas A&M University-Corpus Christi on April 25. The presentation titles were as follows:

• Sabin Pandey: Evaluation of SfM-MVS Apple LiDAR Data for Coastal Monitoring

• Ahmed Omar: Monitoring Groundwater Levels in Different Texan Aquifers Using Satellite Data, Geologic Inputs and Machine Learning

• Mohammad Sohail: Satellite-Based Monitoring and Mapping of Disaster Risk: A Case Study of the Southern Flood Plain in Pakistan

The annual symposium serves as a platform for supporting student research and creative work, helping students achieve their academic and career goals. Participation encourages students to showcase their research ideas, discoveries, and creative work and receive meaningful feedback from an evaluation panel of established TAMU-CC faculty members and researchers. Participation also prepares students for presentations at national and international events.

ASPRS Student Chapter Launched at Texas A&M University-Corpus Christi

We are proud to announce that the American Society for Photogrammetry and Remote Sensing (ASPRS) has officially recognized Texas A&M University-Corpus Christi as an ASPRS Student Chapter. This milestone comes after a successful petition led by the MANTIS Lab director and a group of dedicated graduate students passionate about advancing geospatial sciences.

ASPRS is a prominent scientific association committed to the advancement of Remote Sensing, Photogrammetry, and Geographic Information Systems (GIS). With this new chapter, the Island University joins a distinguished network of academic institutions working collaboratively to support the growth and professional development of the next generation of geospatial scientists.

The TAMU-CC ASPRS Student Chapter aims to provide a platform for students to engage in scholarly exchange, technical training, and outreach initiatives. Through workshops, guest lectures, and networking events, the chapter will foster a vibrant community of learners and practitioners devoted to geospatial innovation.

This accomplishment reflects the growing momentum of geospatial research and applied technology on campus, particularly through initiatives led by the MANTIS Lab. We look forward to the chapter’s contributions in shaping coastal, environmental, and urban research through state-of-the-art remote sensing and photogrammetric methodologies.

For more information, visit our official ASPRS chapter page and view the announcement on LinkedIn.

Benjamin Ghansah Publishes in Computers and Electronics in Agriculture

MANTIS Ph.D. student Benjamin Ghansah has recently published an article in Computers and Electronics in Agriculture titled “Satellite vs Uncrewed Aircraft Systems (UAS): Combining High-Resolution SkySat and UAS images for Cotton Yield Estimation.” The manuscript explores how imagery from a high-resolution satellite system (SkySat) compares with imagery captured by UAS platforms for the purpose of estimating cotton yield. The dataset was collected over cotton fields in Texas during the 2023 growing season. A range of vegetation indices (including NDVI, GCI, MSR) were derived from both satellite and UAS images and used as inputs to a multilayer perceptron (MLP) deep-learning model, with observed yield data (lint + seed) serving as the response variable.

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The abstract reads as follows:

Uncrewed Aircraft Systems (UAS) are widely used for crop growth monitoring and yield estimation in Precision Agriculture (PA). However, UAS are limited by their relatively small area coverage, high cost, and high data processing needs. High resolution satellites (such as SkySat) are valuable alternatives to UAS in PA. Nonetheless, persistent cloud cover, especially in regions like the South of Texas, limits their utility. This study compared and explored the integration of satellite and UAS imagery for cotton yield estimation. The rationale was to determine the best performing platform among the two, as well as leverage their synergy to mitigate data gaps caused by persistent cloud cover. Using deep learning model, vegetation indices derived from SkySat and P4M (Phantom 4 Multispectral) images were correlated with crop yield data collected during the 2023 season. Results demonstrated that SkySat slightly outperformed P4M in yield estimation, with median accuracies of R2 = 0.81 and RMSE = 0.20 ton/ha for SkySat, compared to R2 = 0.80 and RMSE = 0.21 ton/ha for P4M. More importantly, when all the SkySat and P4M datasets were combined, accuracy improved by 3% compared to SkySat-only data. In addition, data collected between 74 and 114 days after planting contributed most significantly to yield prediction. The fusion approach used in this study allows for better spatial and temporal coverage, ultimately enhancing yield prediction reliability in PA. Future research should explore the inclusion of additional sensors such as Synthetic Aperture Radar (SAR) and thermal imagery, which could further improve yield prediction accuracy, especially in cloud-prone regions.