This presentation outlines the evolution and operational framework of the Massachusetts Department of Transportation (MassDOT) Drone Program, established in 2017. The program strategically integrates Unmanned Aerial Systems (UAS) to enhance transportation infrastructure management across multiple domains, including emergency response, aeronautics, highways, and rail and transit systems. Central to this effort is the development of a scalable and secure approach for capturing, processing, managing, and delivering aerial data as actionable intelligence to support decision-making. The presentation will highlight the end-to-end MassDOT UAS workflow, including mission planning, data acquisition, processing pipelines, quality assurance/quality control (QA/QC), and enterprise data dissemination. A key focus will be the implementation of the Aeronautics Data Hub—an enterprise UAS data management solution.
Dr. Sinan Abood is a GIS Scientist and Environmental Engineer with over 15 years of experience in geospatial modeling, remote sensing, and natural resource management. He serves as Aeronautics Data & Analytics Chief at MassDOT Aeronautics, leading UAS data processing, LiDAR analytics... Read More →
Tuesday May 26, 2026 3:15pm - 3:45pm EDT Auditorium
Many organizations have infrastructure information in drawings, PDFs, spreadsheets, and older records, but still struggle to turn that material into GIS people can actually use. This presentation will look at the space between data conversion and day-to-day use, and why converted infrastructure data is often difficult to work with in practice. It will also consider how the pressure to make messy records look complete can undermine long-term editing, maintenance, and decision-making. The session will center on the kinds of decisions that affect whether the final GIS is something people can trust and maintain. It is intended for anyone working with infrastructure data that needs to become more usable over time, not just more digital.
Local governments maintain thousands of infrastructure assets across the right of way, but the data behind those assets is often incomplete, inconsistent, or years out of date. Updating it typically means manual field surveys, disconnected spreadsheets, and time that GIS teams do not have. This session looks at how computer vision and machine learning models trained on street-level imagery and LiDAR point clouds can automate the production of structured, geospatially referenced infrastructure data: pavement condition scores and distress classifications, sign inventories coded to MUTCD standards, pavement markings, and right of way assets like hydrants, poles, and curbing. The session walks through the full data pipeline from sensor to GIS-ready outputs, including how imagery is collected, how models detect and classify assets with spatial precision, and how the resulting datasets integrate with existing tools through standard export formats. For GIS professionals supporting public works teams, the practical question is whether this approach fits into current workflows and what it changes about the quality, coverage, and frequency of the data they manage. This session addresses that directly.
