From floodplain management to transportation corridor monitoring, today’s most pressing infrastructure and environmental challenges demand smarter, faster, and more integrated geospatial solutions. This session brings together experts leveraging the latest in mobile GIS, topobathymetric LiDAR, and Geo AI to transform how we model, manage, and protect critical systems.
Presentations will explore how mobile GIS and hydraulic modeling can work together to enable real-time, cloud-based water resource management, improving accuracy and efficiency in field-to-model workflows. Attendees will also learn how compact UAS-based topobathymetric LiDAR systems are revolutionizing floodplain mapping and mitigation design—delivering dense, high-resolution data that inform safer, more resilient communities. Finally, the session will highlight how Geo AI and remote sensing are being combined to monitor transportation corridors at scale, providing actionable insights into environmental risk, slope stability, and land-use change.
Together, these case studies showcase how innovations in geospatial data collection, processing, and analytics are reshaping water and infrastructure management—offering scalable, cost-effective pathways to resilience in the face of climate and operational challenges.
The following presentations will be shared in this session:
Exploring Mobile GIS and Hydraulic Modeling as Emerging Tools for Integrated Water Management
Presented by Amol Kilkarni, CDM Smith
Hydraulic modeling is a cornerstone of modern water resource management, supporting applications such as flood risk assessment, stormwater infrastructure design, water supply optimization, and climate adaptation planning. However, the effectiveness of these models is highly dependent on the quality and timeliness of field data. Traditional workflows—characterized by manual data collection, delayed processing, and asynchronous communication between field teams and modelers—introduce inefficiencies and increase the risk of data inaccuracies.
This paper presents an integrated, mobile GIS-based framework that enables real-time data collection, cloud-based synchronization, and dynamic hydraulic model updates. Field personnel utilize mobile applications such as Collector for ArcGIS and Survey123 to capture geospatial and attribute data, including site photos, GPS coordinates, and infrastructure condition reports. These data are automatically uploaded to a centralized cloud platform (e.g., ArcGIS Online), where modelers can access and incorporate them into hydraulic simulations with minimal latency.
The proposed system architecture supports cross-platform accessibility (iOS, Android, Windows) and incorporates advanced visualization tools, including 3D model rendering and interactive dashboards. This approach was validated through a case study on the Performance Assessment of Irrigation Canal Systems, where canal deficiency data collected via mobile GIS was integrated with a canal hydraulic model hosted on ArcGIS Online. The results demonstrated significant improvements in data accuracy, model responsiveness, and operational efficiency. This study highlights the transformative potential of mobile GIS and cloud-based platform as emerging technologies in the domain of water resource engineering, offering a scalable and cost-effective alternative to conventional modeling practices.
Changing Flows – Multi-Temporal Topobathy Surveys for Dam Removal
Presented by Paul Burrows, Whiteout Solutions
Since first acquiring it’s topobathy lidar sensor, Whiteout Solution has been surveying dam removal projects. In some instances the projects have required pre removal and post removal surveys to help team understand the change in these ecosystems. The UAS topobathy platform has given us the opportunity to see these dynamic systems change in high resolution. For the initial collection, the data serves as a baseline of the impounded area, dam structure and floodplain to identify sediment deposits and characteristics of the river channel. The resulting data can be used in hydraulic models to simulate sediment transport post dam removal. Subsequent surveys post dam removal, the data provides validation of predictions from the modeling of the pre-removal survey as well as monitoring the geomorphic changes of the river channel.
Geo AI Analysis of Airborne Imagery and Lidar for Transportation Corridor Management and Risk Monitoring
Presented by Dr. Aaron Morris, Woolpert
Maintaining road and rail infrastructure across large and often remote areas presents significant challenges for transportation agencies, owners, and operators. Traditional inspection methods can be labor-intensive, reactive, and limited in spatial coverage—making it difficult to monitor environmental conditions, land use changes, and geotechnical risks at scale. This presentation shows how the integration of artificial intelligence with geospatial sensing technologies (Geo AI) enables the analysis of high-resolution airborne imagery and lidar data to deliver scalable, proactive insights into adjacent land use, habitat conditions, and landslide risk along road and rail corridors.
Transportation corridor applications such as environmental monitoring, vegetation management, and regulatory compliance support require Geo AI to perform analysis at scale. In this presentation, we will detail how Geo AI models are trained, evaluated, and their output used to score environmental, biodiversity, and corridor-adjacent risks. Specific examples of scenarios such as slope stability and surface change using lidar-derived terrain models, hydrologic flow patterns, and machine learning-based risk scoring to identify areas susceptible to landslides or erosion will be covered.
By attending this session, participants will gain practical insight into how Geo AI and remote sensing technologies are transforming the way road and rail infrastructure is monitored and maintained. The presentation will showcase real-world examples of how complex geospatial data can be turned into meaningful, decision-ready information—enabling earlier detection of risks, more informed resource allocation, and greater environmental accountability. Whether you’re focused on safety, sustainability, or operational efficiency, this talk offers a forward-looking perspective on how to overcome the scale and remoteness challenges facing modern road and rail systems with cutting-edge tools that are ready to deploy today.
Case Study of Efficient Point Cloud Utilization Workflow in Hydropower Infrastructure
Presented by Nate Soulje, Elysium, Inc.
AtkinsRéalis is a world-leading design, engineering, and project management organization with 100+ years of experience within the company.Some of the challenges that AtkinsRéalis has faced using point clouds include managing data size, software fragmentation, inefficient workflows, and more that are related to using large datasets.In this session, we will introduce a case study of a hydropower project where AtkinsRéalis overcame these challenges by using InfiPoints to streamline their point cloud processing workflow.In particular, we will explain the effectiveness of point cloud utilization in facility renovation and equipment replacement including practical tools such as automatically extracting pipes, walls, steel structures and more.Then using built-in modeling tools to prepare the extracted geometry for downstream CAD systems.We will further demonstrate that AtkinsRéalis would compare existing models against their captured point cloud to automatically recognize differences between the as-designed vs as-built condition thus saving significant time in updating existing models and planning future projects.In this session, we will focus on working with CATIA V5 as the CAD system used in the specific case study.This session is ideal for civil infrastructure, energy, and construction industry professionals looking for a scalable and practical solution to maximize the value of their point cloud data.
