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.

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.

MANTIS and TTI Collaborate on GNSS Evaluation

The Measurement Analytics (MANTIS) Laboratory recently joined forces with the Texas A&M Transportation Institute (TTI) for a hands-on geospatial research experiment aimed at advancing the understanding of specific GNSS technologies. This collaborative effort brought together researchers and engineers from both teams to conduct a comparative field study of GNSS receiver performance across various grades and correction techniques.

The joint fieldwork took place on the Texas A&M University campus, where teams collected GNSS data over a known benchmark. The objective was to evaluate the feasibility, limitations, and practical applications of different GNSS configurations in the context of geospatial sciences, surveying and mapping, and transportation-related projects. By comparing the different setups, the researchers seek to quantify positional accuracy, reliability under varying environmental conditions, and overall cost-effectiveness.

Key considerations included accuracy and precision (how close each receiver-correction pair could measure relative to the benchmark), operational complexity (time, setup, and training required to deploy each system), and cost benefit (balancing budget constraints with positional accuracy needs). Findings are expected to inform best practices for selecting GNSS technologies based on project requirements, whether for asset inventory, roadway mapping, traffic infrastructure planning, or other geospatial applications where precise positioning is critical.

This collaboration underscores the value of interdisciplinary research and practical testing as MANTIS and TTI continue to bridge engineering and geospatial science to support innovation in transportation systems and beyond.

MANTIS Faculty and Students Present Research at ASPRS Gulf South Conference

MANTIS faculty and students recently attended the American Society for Photogrammetry and Remote Sensing (ASPRS) Gulf South Conference, held March 13-15 in Austin, Texas. The event provided an opportunity for researchers to share advancements in remote sensing and geomatics, particularly in the areas of UAS-SfM photogrammetry, LiDAR, artificial intelligence, and InSAR applications. The MANTIS team contributed to the discussion with several compelling presentations, each exploring innovative methods to enhance surveying, mapping, and geospatial analysis.

MANTIS director, Dr. Michael J. Starek delved into the role of artificial intelligence and deep learning in UAS photogrammetric workflows. His research highlights how AI-driven automation can significantly enhance data processing, feature extraction, and accuracy assessment in aerial photogrammetry. The ability to integrate deep learning into these workflows paves the way for more efficient and scalable mapping solutions, reducing manual labor while improving the quality of 3D reconstruction. MANTIS associate director, Dr. Tianxing Chu (joined by CBI’s research scientist, Dr. Danielle Smilovksy), provided insights into InSAR technology, emphasizing its applications for infrastructure monitoring, land subsidence detection, and disaster response. Their presentation addressed the current capabilities of InSAR and how emerging advancements in satellite-based remote sensing will further improve the technology’s ability to monitor surface deformation with high accuracy.

Graduate students Benjamin Gansah, Jose Pilartes-Congo, and Sabin Pandey also presented their research. Benjamin introduced his research on the integration of remote sensing and machine learning for agricultural monitoring. His work leverages advanced image processing and classification techniques to assess crop health and yield predictions. By combining remote sensing data with machine learning models, his study provides a pathway for smarter, data-driven agricultural decision-making, addressing challenges such as food security and resource optimization. Jose presented a study comparing UAS-SfM photogrammetry and UAS-LiDAR for material volume estimation. His research aimed to determine which method offers greater accuracy and efficiency in measuring material volumes. By evaluating the strengths and limitations of both techniques, his findings contribute to the ongoing optimization of remote sensing workflows. Finally, Sabin discussed his research on the accuracy of Apple lidar scanning patterns using the SfM/MVS photogrammetry techniques. His research focused on assessing how well Apple’s mobile lidar systems perform compared to traditional UAS-SfM photogrammetry approaches. He also explored the potential for data fusion between Apple lidar and UAS imagery, which could open new possibilities for cost-effective, high-resolution 3D mapping applications.

The MANTIS research group continues to push the boundaries of geospatial science, developing methodologies that improve the accuracy, efficiency, and scalability of remote sensing applications. Their work at the ASPRS Gulf South Conference reflects their ongoing commitment to advancing the field and contributing to real-world solutions in surveying, mapping, and environmental monitoring. As remote sensing technology evolves, the MANTIS team remains at the forefront, driving innovation for both academic research and industry applications.

Naval Research Lab Representatives Visit MANTIS

In a recent visit to the Conrad Blucher Institute, representatives from the Naval Research Laboratory (NRL) spent some time in the MANTIS Laboratory for a brief discussion on the latest advancements in remote sensing technologies and artificial intelligence (AI) for surveying and mapping. The visit focused on the various ways in which MANTIS researchers are exploring cutting-edge methodologies to enhance 3D data collection and geospatial analysis, with focus on coastal monitoring. During the visit, the MANTIS team presented their ongoing projects, demonstrating how remote sensing techniques are being integrated with AI to improve data collection, processing, and analysis. The discussion highlighted the application of these technologies for coastal surveying, asset monitoring, and long-term management.

TAMUCC Alumni Inspire Future Land Surveyors

During a recent recruitment visit to Texas A&M University-Corpus Christi (TAMUCC), former students, Juan Martinez (‘17), Luis Hernandez (‘17), and Dustin Pustejovski (‘14), who are now professionals at Westwood Professional Services, Inc., returned to share their insights and experiences in the land surveying profession. The visit provided a valuable opportunity for current students to connect with industry professionals and learn about the latest advancements in the field. The alumni engaged with students, former colleagues, and instructors, offering firsthand perspectives on their career paths and the evolving landscape of land surveying. Their experiences highlighted the practical applications of their education and the diverse opportunities available within the profession.

A key highlight of the discussion was the integration of cutting-edge technology in modern land surveying practices. The former students showcased how they are using advanced 3D laser scanning and UAS photogrammetry in their projects. These technologies are transforming the industry by enhancing precision, efficiency, and data collection capabilities in various surveying applications. The visit not only served as an informative session for students but also as an inspiring moment for faculty and staff, reinforcing the importance of real-world applications in academic learning. By bridging the gap between education and professional practice, these engagements help students better prepare for their careers and stay informed about industry trends.

Celebrating Dr. Bradley Koskowich's Graduation

Dr. Bradley Koskowich became the latest MANTIS and Geospatial Computer Science program graduate this past December 14, 2024. This is a well-deserved accomplishment for a student who has gone through the geospatial undergraduate and graduate programs at TAMUCC. During his time as a doctoral student, Bradley published several journal and conference papers focused on remote sensing and computer vision techniques for various geospatial applications. We wish Bradley all the best in his future endeavors.

José Pilartes-Congo Defends Dissertation Proposal

MANTIS Ph.D. student José Pilartes-Congo recently defended his dissertation research proposal titled “Change Detection and Digital Twin Generation from Multi-Sensor and Multi-Scale 3D Data Fusion”.

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His abstract read as follows:

Reality capture technologies incorporating 3D scanning and imaging techniques provide innovative and efficient means for measuring the geometric characteristics of built and natural environments. Common techniques include structure-from-motion / multi-view stereo (SfM/MVS) photogrammetry and lidar scanning, which provide dense and informative 3D point clouds, textured meshes, and digital elevation models (DEMs) that can be used to create digital records and repositories of geospatial data, thus supporting surveying and mapping efforts. These methods can be implemented on various remote sensing platforms such as uncrewed aircraft systems (UASs), traditional aircraft, or satellites to offer different extents of coverage, spatial resolution, and measurement accuracies. By facilitating accurate and effective monitoring of structural and environmental changes over time, these techniques can support a wide range of tasks such as project planning, asset management, and natural resource allocation. However, to effectively do so requires determining how to best exploit information captured in 3D data streams acquired from various remote sensing modalities, while addressing differences in data characteristics, measurement fidelity, and task suitability for different applications. This research explores the following question: How can reality capture (i.e., remote sensing) technologies providing 3D geospatial data at different perspectives, resolutions, geographical extents, and measurement fidelities be effectively and optimally integrated to support structural change detection and monitoring of built and natural environments? This research acknowledges that individual remote sensing modalities for acquiring 3D geospatial data have various advantages and limitations and examines ways to optimally fuse different datasets in a way that one complements the other, resulting in more informative and useful 3D geospatial datasets for change detection applications. With this consideration, this research explores data acquired from ground, air, and space, using photogrammetry and lidar scanning technologies, as well as associated algorithmic and processing techniques for data calibration, georeferencing, accuracy assessment, and data fusion for generating 3D digital twins of the environment. Ultimately, the research seeks to develop a digital twin framework able to ingest multi-sensor, multi-scale 3D data at different spatial resolutions and temporal frequencies, for geospatial change detection analyses to support surveying and monitoring activities, especially for transportation roadway corridors (built environments) and dynamic coastlines (natural environments).

José Pilartes-Congo Publishes Article in Drones

MANTIS Ph.D. student José A. Pilartes-Congo has recently published an article in Drones titled “Empirical Evaluation and Simulation of the Impact of Global Navigation Satellite System Solutions on Uncrewed Aircraft System–Structure from Motion for Shoreline Mapping and Charting”. The article focuses on empirical testing and simulated evaluation of different GNSS correction techniques on the accuracy of UAS-SfM products.

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The abstract reads as follows:

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Bradley Koskowich Defends Dissertation

MANTIS Ph.D. Candidate Bradley Koskowich successfully defended his dissertation, titled “An Assessment of Methods for Effective Single Camera Resection Solutions to the Cross-view Geo-localization Problem,” on Nov. 5, 2024. Bradley’s research focused on blending remote sensing products, platforms, and digital reality tools with AI techniques to connect the physical world directly with data. Bradley has developed several full-stack software applications for the Conrad Blucher Institute over the years.

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The abstract of his presentation read as follows:

Typical multi-view stereo (MVS) photogrammetry problems have both traditional and deep learning solutions which utilize collections of overlapping imagery to solve for multiple camera positions simultaneously. Structure-from-motion (SfM) workflows achieve this using bundle adjustments, while simultaneous-localization-and-mapping (SLAM) solutions use a similar, pipelined adjustment method. More recent deep learning research such as neural radiance fields and Gaussian Splatting can also enhance typical MVS photogrammetry results, but all approaches still lean on a crucial operation, which is accurate camera position and orientation estimation, also called camera pose. Camera pose information can be collected via external hardware such as the global navigation satellite system (GNSS) and inertial motion units (IMU), derived in a post-processing phase from known ground control points, or estimated in a relative fashion between images. Anything other than relative estimation generally introduces additional cost, complexity, and potential points of failure which can render collected pose information useless. This dissertation addresses the challenges of using only computer vision to accurately compute camera pose independent of typical recording systems, focusing on the specific photogrammetry sub-problem of determining camera pose between single image pairs: one georeferenced aerial image and one terrestrial perspective image with unknown priors. Also called monoplotting, single camera resectioning, or cross-view geo-localization, it is technically a simpler camera configuration to solve than MVS photogrammetry, but it lacks the information density MVS photogrammetry methods usually leverage and is extremely sensitive to initial conditions, making it difficult to solve automatically.

In this dissertation, potential applications that can be built atop accurate monoplotting solutions are demonstrated and enhancements to both algorithmic methods and deep learning architectures for solving the monoplotting problem are explored. First, a practical application demonstrates monitoring vehicular traffic in a parking lot from an existing security camera installation in real time, powered by monoplotting. This practical application also illustrates the extreme sensitivity to initial conditions. Second, an algorithmic approach with a purpose-built feature matching method supported by GPU-accelerated feature extraction and data processing was developed and tested across a variety of environments to gauge its ability to mitigate sensitivity to initial conditions. Finally, insight into the behaviors of deep learning architectures which can partially solve the monoplotting problem was obtained by investigating the effects of replacing dense training collections of georeferenced & pose-tracked terrestrial imagery with historical aerial image collections, achieving comparable or better results with fractional training data compared to prior studies. A hybrid approach that combines deep learning for partial initialization with the algorithmic method is proposed, using less training data to improve computed pose accuracy in full 3D space. The broader impact of this research could allow systems that rely on camera pose estimation to do so in a way that provides it as validation or recovery mechanism independent of typical GNSS/IMU systems in the event of catastrophic failure.

Dr. Mohammad Pashaei Embarks on a New Journey

We are thrilled to announce that Dr. Mohammad Pashaei, a distinguished research scientist at MANTIS, will soon be taking his expertise into the transportation industry. Dr. Pashaei completed his Ph.D. in Geospatial Computer Science at Texas A&M University-Corpus Christi, and his research with the MANTIS Lab specialized in developing advanced machine learning and deep learning frameworks to analyze remote sensing data, leveraging technologies like UAS and lidar.

During his time at MANTIS, Dr. Pashaei made remarkable strides in geospatial information retrieval, building frameworks capable of processing complex remote sensing data for diverse applications. His work not only advanced MANTIS's research initiatives but also set new standards in the field of geospatial analysis and intelligent data extraction. As he transitions into the transportation industry, we are confident that Dr. Pashaei will continue to drive impactful advancements. The skills he honed at MANTIS will be invaluable in addressing challenges in transportation, and we look forward to exploring future collaborations with him.

Thank you, Dr. Pashaei, for your contributions. We wish you every success in this exciting new journey!

CBI and MANTIS Showcase Contributions to Texas Legislature Staff

On October 28, 2024, the Conrad Blucher Institute and MANTIS welcomed Texas Legislature staff to highlight the valuable contributions it makes to the State of Texas.

Dr. Michael J. Starek, the director of MANTIS, led a discussion on how the institute is pioneering the use of geospatial science and technology to enhance data collection for surveying and mapping initiatives across Texas. The presentation provided valuable insights into the lab's innovative technologies and methodologies, focusing on cutting-edge remote sensing technologies such as LiDAR and SfM photogrammetry. These technologies allow for high-resolution spatial data collection and enable more accurate mapping and analysis for various applications, including infrastructure development, transportation asset monitoring, and land management. They also contribute to safer data collection practices compared to traditional surveying methods.

MANTIS is committed to fostering collaborations that enhance the understanding and application of geospatial science. This visit marks another significant step toward strengthening partnerships with state policymakers.

Dr. Mohammad Pashaei Presents Research at the University of Houston

Dr. Mohammad Pashaei, a Research Scientist at MANTIS, recently presented his latest research at the University of Houston, focusing on the application of AI-driven image analysis for more efficient environmental mapping using unmanned aerial systems (UAS) and structure-from-motion / multi-view stereo (SfM/MVS) photogrammetry. Dr. Pashaei highlighted how UAS-SfM offers a cost-effective approach for mapping shorelines and coastal areas, though it faces challenges due to the dynamic and shifting nature of these environments. His work delves into semantically-informed mapping techniques designed to address these challenges, enhancing both the quality and accuracy of the mapping outputs generated through these advanced technologies.