As geospatial data becomes increasingly critical for infrastructure, engineering, digital twins, and asset management, demonstrating accuracy and quality is essential. This session explores the application of ASPRS accuracy standards and emerging best practices for evaluating lidar, photogrammetric, and UAS-derived products, providing practical approaches for implementing defensible QA/QC workflows and building confidence in geospatial deliverables.
12:00 PM – 12:15 PM
Delivering Trusted Geospatial Data: Applying ASPRS Accuracy Standards and Emerging Best Practices Across Modern Mapping Projects
Presented by Qassim Abdullah, Woolpert
As organizations increasingly rely on geospatial data to support infrastructure development, utilities, transportation, digital twins, asset management, and AI-driven decision-making, the ability to demonstrate data quality has become a business necessity—not just a technical requirement. Project owners, contractors, and technology providers alike need practical methods to verify that mapping products meet project specifications, reduce risk, and support confident operational decisions.
This panel session brings together leaders from industry and academia to discuss how the ASPRS Positional Accuracy Standards for Digital Geospatial Data are being successfully applied to today’s mapping projects involving lidar, aerial imagery, mobile mapping, UAS, and vector data. Through real-world examples and lessons learned, panelists will explore how standardized quality assessment improves project outcomes, strengthens client confidence, and provides objective acceptance criteria throughout the geospatial production workflow.
The panel will also provide an update on the work of the ASPRS Standards Committee to develop a new generation of best practices and technical guidelines that complement the positional accuracy standards. These documents address practical topics such as quality assurance and quality control workflows, sampling methodologies, feature completeness, logical consistency, imagery and lidar validation, surface quality assessment, and quantitative evaluation of vector mapping products. Together, these resources are intended to help organizations implement consistent, repeatable, and defensible quality management practices regardless of sensor, platform, or software environment.
12:15 PM – 12:30 PM
How to Apply ASPRS Standards to Improve Accuracy and Confidence in UAS Terrain Models
Presented by Kurt Aper, Atwell
Advancements in photogrammetry and lidar have made high-resolution mapping more accessible than ever, but ensuring the accuracy and consistency of those datasets remains a persistent challenge. While many organizations collect high-quality data, fewer have standardized processes for evaluating, documenting, and communicating positional accuracy.
This session introduces the ASPRS Positional Accuracy Standards and demonstrates how they can be integrated into UAS mapping workflows to establish repeatable quality assurance practices. Through practical examples, attendees will learn how to assess positional accuracy, interpret results, and communicate data quality with confidence. Whether producing topographic surveys, terrain models, or engineering deliverables, participants will leave with actionable strategies to improve the reliability and defensibility of their geospatial products.
12:30 PM – 12:45 PM
Cost and Accuracy Analysis of UAS Photogrammetry and UAS Lidar with a Focus of the Surveying and Mapping Industry
Presented by Charlie Schreiner, ETM Surveying and Mapping, and Youssef Kaddoura, University of Florida
Unmanned Aerial Systems (UAS) have transformed topographic surveying & mapping, with two primary remote‑sensing methods: (1) airborne light detection and ranging (lidar) and (2) structure‑from‑motion (SfM) photogrammetry from high‑resolution imagery. While lidar is widely considered the benchmark for vertical accuracy, particularly where canopy or vegetation exists, its capital and operational costs appear to be higher.
Photogrammetry is less expensive to field, yet its accuracy depends greatly on image geometry, camera calibration, environmental factors, and georeferencing strategy. This paper analyzes a practice‑oriented comparison: (a) an operational labor/effort analysis for two theoretical survey projects (an approximate 2,000‑ft paved corridor and an approximate 1,000‑acre tract), and (b) vertical accuracy testing on hard, paved surfaces using independent check points leveled to NAVD88 (North American Vertical Datum 1988) and tied horizontally to NAD83-2011 (North American Datum 1983, 2011 Revision) Florida State Plane East USFT via FDOT-FPRN GNSS – RTK (Florida Department of Transportation – Florida Permanent Reference Network Global Navigation Satellite System – Real-Time Kinematic). Across four independent survey project estimator worksheets structured by 10‑hour field days and office hours, time savings from photogrammetry proved modest when full professional workflows were included.
Accuracy testing with TopoDOT software indicated consistently lower vertical residuals for lidar for all testing parameters, with photogrammetry possibly improving under best‑practice planning but retaining greater variability. Findings are interpreted with the ASPRS Positional Accuracy Standards and USGS Lidar Base Specification (LBS) requirements, and within the Florida professional practice environment (Florida Statutes § 177.501 et seq., 2022). A decision framework and actionable recommendations are offered for choosing between lidar and photogrammetry by task, site conditions, and accuracy requirements.
12:45 PM – 1:00 PM
USGS Horizontal Lidar Density Assessments for 3DEP Data
Presented by Barry Miller, U.S. Geological Survey
The National Geospatial Technical Operations Center and the Earth Resources Observation and Science Center of the U.S. Geological Survey (USGS) conducted a comparative analysis of lidar horizontal density assessment workflows using multiple commercial software packages and a custom Python implementation, applying all approaches to a common set of lidar LAS files in New York and Colorado. The goal of this evaluation is to understand how these tools align with USGS production workflows, how threshold-based decision rules can be designed, and how density requirements might be incorporated into the USGS Lidar Base Specification.
The study examines consistency in density outputs, pass/fail criteria, and reporting transparency across anonymized commercial software and a reference Python workflow, with particular focus on the Voronoi-based sampling density method described in the ASPRS Horizontal Sampling Density of Aerial Lidar Point Cloud Data guidelines. Each implementation was configured to a 2 points per square meter threshold using last-or-only returns and no excluded classes.
Across the test datasets, the commercial tools produced nearly identical project-level pass/fail outcomes, while the Python workflow provided fully traceable numerical detail and served as the reference for point-level Voronoi density estimates. Strong agreement in modal density values across analysis platforms confirms the consistency of the core Voronoi computation. Operational differences were also observed: some commercial tools emphasized standardized reporting aligned with ASPRS-style workflows, others prioritized processing speed with limited auditability, while the Python workflow provided transparent computation and flexible decision-support layers.
The comparison identifies persistent issues—including boundary sensitivity, duplicate handling, and tile-level validation—that affect density reliability across implementations. These findings will inform the development of thresholds, guide evaluation of software fit within USGS Data Validation workflows, and support efforts to include density-related requirements in the Lidar Base Specification.
This presentation will summarize comparative results, discuss implications for 3D Elevation Program (3DEP) acceptance criteria, and recommend next steps for validating horizontal density assessment workflows. The work underscores the importance of transparent, repeatable, auditable methods for reference implementation, and standardized reporting practices for lidar QA/QC.
