Rivers, reservoirs, and coastal waterways are some of the most demanding environments in geospatial work. Shallow water, rapidly changing stage, variable clarity, and the overlap between land and water disciplines all affect what a sensor can observe and whether datasets collected at different times describe the same surface. Getting reliable results takes more than good equipment. It takes careful planning, thoughtful data integration, and a clear understanding of what the final product does and does not represent.
This session brings together two presentations on producing better topobathymetric data in these settings. The first looks at how inland survey teams adapt marine hydrographic practices to river environments, integrating sonar, lidar, imagery, and conventional survey data while accounting for water levels, acquisition timing, and uncertainty. The second explores how geoAI-driven processing and in-flight monitoring can improve airborne lidar collection and analysis across both land and water, from acquisition strategy to feature extraction.
This session will include the following presentations:
Hydrography and Data Integration on Inland Rivers
Presented by Joshua Price, Prairie Engineers
Hydrographic surveying practices, equipment, and quality standards have largely developed around navigation and offshore environments. Inland waterways present a different operating environment. Rivers, reservoirs, locks, backwaters, and restoration sites combine shallow water, narrow corridors, rapidly changing stage, strong currents, turbulence, multipath, submerged vegetation, irregular shorelines, limited launch access, and frequent overlap between land and water surveying disciplines.
These conditions affect more than vessel operations. They determine what each sensor can observe, when it can observe it, and whether datasets collected at different times represent the same physical surface. A complete river model may incorporate multibeam and single-beam sonar, topographic or topobathymetric lidar, aerial imagery, photogrammetry, conventional survey observations, water-level records, and field photographs. Each source has different accuracy, density, spatial coverage, acquisition dates, and environmental limitations.
This presentation will examine how inland survey teams adapt marine hydrographic workflows to produce defensible topobathymetric deliverables. Topics will include mission and line planning, sensor and vessel selection, positioning constraints, water-surface and vertical-datum control, temporal normalization, overlap analysis, breaklines, interpolation, vegetation and noise removal, uncertainty attribution, and the treatment of gaps between datasets.
Particular attention will be given to river stage and acquisition timing. Imagery collected during high water may depict an open, navigable corridor that contains exposed shoals, vegetation, rock fields, or other hazards at the time of survey. Likewise, datasets collected months or years apart may represent different shorelines, sediment conditions, water surfaces, and channel morphology. Combining them without documenting those differences can create a visually seamless model that appears more complete and accurate than the underlying observations support.
Attendees will leave with a practical framework for planning inland hydrographic surveys, integrating land and water datasets, documenting the hydrologic conditions represented by each source, and communicating where the final surface is measured, interpolated, temporally adjusted, or uncertain.
Advancing Lidar-Derived Analytics Using geoAI / geoSAP
Presented by Matthew Clark, Whiteout Solutions LLC
Helicopter-based lidar acquisition in coastal and riverine environments generates complex datasets that are often underutilized due to fragmented processing workflows and limited analytical integration. This presentation focuses on research using the geoSAP geoAI framework to unlock higher-value analytics from lidar data by improving data quality, reducing processing time, and enabling more robust feature extraction across both topographic and bathymetric domains.
The Housatonic River (MA) project serves as a primary research site, where challenging environmental conditions—including variable water clarity, depth, and bottom reflectivity—provided a rigorous testbed for evaluating data-driven improvements. We demonstrate how enhanced processing workflows lead to more reliable surface classification, improved bottom detection, and greater consistency between terrestrial and submerged features. These improvements directly support higher-confidence derived products and more detailed environmental analysis.
A key component of this work is the integration of geoSAP’s field planning and monitoring system to inform acquisition strategy. In-flight awareness of return quality enabled adaptive decision-making, allowing operators to refine flight parameters, address coverage gaps, and maximize usable data collection within the area of interest. This tighter coupling between acquisition and processing further strengthens downstream analytics by ensuring more complete and higher-quality datasets.
This research highlights how integrated processing and acquisition awareness can significantly elevate the analytical value of airborne lidar, positioning geoSAP as a framework for more data-driven and insight-rich mapping workflows.
