Mohammad Pashaei

TTI Assistant Research Scientist

Short Biography

Dr. Mohammad Pashaei is an Assistant Research Scientist at the Texas A&M Transportation Institute (TTI). Before joining TTI, he was an Associate Research Scientist at the Conrad Blucher Institute (CBI) for Surveying and Science at Texas A&M University–Corpus Christi, where he led multiple research studies leveraging Artificial Intelligence (AI), including machine learning (ML) and deep learning (DL), for advanced geospatial analytics and modeling.

Dr. Pashaei earned his Ph.D. in Geospatial Computing Science from Texas A&M University–Corpus Christi in 2021, with research focused on applying deep learning and multi-perspective 2D/3D imaging streams for remote terrain characterization in coastal environments. His primary research interests span active and passive remote sensing, geospatial data fusion, geospatial modeling, AI-driven geospatial analytics, photogrammetry, image processing, machine vision, light detection and ranging (LiDAR), full-waveform LiDAR, and uncrewed aircraft systems (UASs) for environmental mapping.

Earlier in his career, Dr. Pashaei developed and implemented AI-based data processing workflows for automated geospatial information retrieval and high-resolution 2D/3D mapping from remote sensing data. His work includes utilizing deep learning frameworks to process UAS imagery and LiDAR data for automated land cover mapping, applying DL for full-waveform LiDAR target detection and land cover analysis, enhancing Structure-from-Motion (SfM) photogrammetry workflows for efficient 3D scene reconstruction, implementing DL-based super-resolution techniques for efficient environmental mapping with UAS imagery, and employing DL for 3D scene segmentation in raster data.

Dr. Pashaei’s research contributions have been published in esteemed journals such as Remote Sensing (MDPI) and IEEE Transactions on Geoscience and Remote Sensing (ITGRS). He serves as a peer reviewer for journals including ITGRS, Remote Sensing (MDPI), Canadian Journal of Remote Sensing, and Journal of Surveying Engineering, and has chaired committees for the IEEE IGARSS conference.

He has also contributed to TxDOT-funded research at TTI, including projects like Develop Guidelines for Integration of UAS LiDAR and Photogrammetry to Enhance Land Surveying Capabilities and Guidance for the Use of UAS During Suboptimal Environmental Conditions. Currently, Dr. Pashaei is engaged in several TxDOT research initiatives aimed at enhancing utility investigations through AI-driven data fusion, establishing quality assessment standards, and defining data collection requirements and strategic research pathways to support the digital delivery program.