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February 10-12, 2025  |  Colorado Convention Center   |  Denver, CO, USA

Session Details

Aevex Aerospace Lidar

Poster Session II

Feb 12 2025

10:00 AM - 10:30 AM MT

Future Leaders Hub

10:00 – 10:06 AM – ECLAIR: A High-Fidelity Aerial Lidar Dataset for Semantic Segmentation
We introduce ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LiDAR dataset designed specifically for advancing research in point cloud semantic segmentation. As the most extensive and diverse collection of its kind to date, the dataset covers a total area of 10km2 with close to 600 million points and features eleven distinct object categories. To guarantee the dataset’s quality and utility, we have thoroughly curated the point labels through an internal team of experts, ensuring accuracy and consistency in semantic labeling. The dataset is engineered to move forward the fields of 3D urban modeling, scene understanding, and utility infrastructure management by presenting new challenges and potential applications. As a benchmark, we report qualitative and quantitative analysis of a voxel-based point cloud segmentation approach based on the Minkowski Engine.
Kristy McDermott, Sharper Shape

10:06 – 10:12 AM – Mapping Geothermal Zones With Airborne Thermal Infrared
In recent years, the increasing frequency and intensity of flood events have necessitated advanced techniques to safeguard-built assets and mitigate potential damage in settlement areas. Traditional methods of flood assessment, which tend to rely on time-consuming physically based models, have proven insufficient in the face of rapidly evolving climate conditions. Consequently, advancements in artificial intelligence (AI) and geospatial technology have significantly improved the rapid assessment of flood impacts. These techniques enable accurate estimation of flood extents and flood water depths, providing valuable data for proactive and sustainable flood management. Our study leverages deep learning algorithms, specifically focusing on conditional generative adversarial networks (cGANs) and geospatial datasets to simulate 3D floodwater depth in areas affected by Hurricane Matthew in Lumberton, North Carolina. By integrating cGANs with high-resolution post-flood UAV imagery, lidar DEM (elevation, aspect, slope, curvature), landcover/landuse data, and rainfall records, we aim to create a comprehensive and precise model for floodwater depth mapping. High-resolution UAV imagery captures detailed flood-affected areas, while lidar DEMs provide crucial topographical data for modeling floodwater distribution and accumulation. Built-up area data assesses the interaction between floodwaters and human-made structures, and rainfall data enhances the predictive accuracy of our model. This integration significantly reduces computational time compared to traditional physically based models, enabling rapid and accurate flood depth simulations crucial for emergency response and sustainable planning. The effectiveness of this approach in Lumberton illustrates its potential to inform proactive flood management practices, contributing to the resilience and sustainability of flood prone urban areas. This work was supported in part by NOAA award NA21OAR4590358, NASA award 80NSSC23M0051, and NSF grant 1832110.
Mousa Diabat, NV5 Geospatial Inc.

10:12 – 10:18 AM – 3D Floodwater Depth Analytics With Big Geo-Data and cGAN
Accurate and up-to-date wetland maps are critical for conserving wetlands on a landscape scale. The most effective methods for creating these maps are those that are automated, scalable, repeatable, and widely applicable. However, wetlands vary significantly across different regions and over time, making it challenging to map them using predictive models. Currently, most approaches are limited by geographical specificity, reliance on commercial data, and low resolution. This research aims to develop a deep learning model to evaluate its effectiveness in mapping wetlands across the Columbia, North Carolina landscape. The study employs a U-Net model and trains the model using UAV imagery, multispectral data from the National Agriculture Imagery Program, Sentinel-2 imagery, and two LiDAR-derived datasets to generate wetland maps at a 1-meter spatial resolution. The results demonstrate how freely available data and advanced deep learning techniques can achieve high-resolution mapping without manual feature engineering. Given the dynamic nature of wetlands and their vital ecosystem services, such high-resolution mapping can transform decision-making in development and restoration projects. This work was supported in part by NOAA award NA21OAR4590358, NASA award 80NSSC23M0051, and NSF grant 1832110.
Jeffrey Blay, NC A&T University

10:18 – 10:24 AM – Wetland Classification With Multispectral and Lidar Data Using Deep Learning
The NC DWR is currently working with several stakeholders in the Yadkin Pee Dee River Basin to establish new rules on nutrient loads to improve water quality in High Rock Lake, NC. Measuring the impact of management interventions is critical to assess the effectiveness of large financial investments and continuously improve nutrient management strategies. Remote sensing data from satellites and drones can be translated into time series maps of chlorophyll-a (Chl-a), total suspended solids (TSS), and color dissolved organic matter (CDOM) that cover the entire lake. These maps can supplement sparse in-situ water quality datasets and enable more holistic and comprehensive management plans that consider spatial and temporal variability, patterns, and trends. In this presentation, we will describe a multifaceted research project that aims to map water quality in High Rock Lake using medium resolution Sentinel-2 imagery, high resolution PlanetScope imagery, and UAV-collected imagery. We have focused on mapping Chl-a, the parameter used to regulate nutrient concentrations and considered a measure of algal bloom occurrence risk. We will describe the statistical models used to relate remote sensing reflectance to Chl-a and present Google Earth Engine Applications and an ArcGIS data portal and dashboard that we are using to disseminate results. We regularly meet with state water quality managers to solicit feedback and iteratively develop these public-facing data repositories. This project is funded by the North Carolina Attorney General’s Office Environmental Enhancement Grant Program.
Matilda Anokye, NC A&T University

10:24 – 10:30 AM – Using Remotely Sensed Imagery for More Frequent and Comprehensive Water Quality Monitoring in High Rock Lake, NC
Thermal infrared (TIR) surveys were conducted over multiple sites to characterize and map existing thermal zones, establishing a baseline against which any future changes may be compared. The sites were the area surrounding Casa Diablo geothermal Power plant in Mammoth Lakes, CA, and along the scenic highway in Yellowstone, WY. These data collection campaigns were part of an ongoing monitoring program to assess the prsence and potential changes in the natural surface expression of the geothermal system. This presentaiton will descibe the data collection techniques and findings.
Leila Hashemi-Beni, NC A&T University

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NC A&T University

NC A&T University

NC A&T University

Sharper Shape

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