Processing, managing and publishing the substantial volume of data collected through modern remote sensing technologies in a format that is easy for researchers - across broad skill levels and scientific domains - to view and use presents a formidable challenge. As a prime example, the massive scale of image mosaics produced by NEON’s Airborne Observation Platform (AOP), often several to hundreds of gigabytes in volume, demands efficient data management strategies. Additionally, these aerial mosaics frequently exhibit seams due to variations in lighting conditions during the data acquisition process. These seams undermine the integrity of subsequent scientific analyses, introducing distortions that hinder accurate interpretation of ecological patterns. Finally, one of NEON’s core objectives is to make these data broadly accessible to users, including those who are not yet versed in working with remote sensing data or who wish to view the datasets without needing to download and process them.In response to these challenges, we have developed a comprehensive data management pipeline that enables interactive access for analysis and visualization of NEON’s aerial mosaic collection. This pipeline automates data ingestion, conversion, and publication in a streamable format, facilitating seamless user interaction through web viewers and programming APIs. Moreover, we have implemented a portable blending algorithm aimed at eliminating these problematic seams from large aerial mosaics. This algorithm, grounded in the Conjugate Gradient (CG) method, has been implemented both in CUDA and using the modern SYCL programming model for enhanced portability across diverse computing platforms.Experimental results demonstrate scalable performance across both CPU and GPU architectures. This work not only addresses the challenges of large aerial data management and seam removal but also opens avenues for more accurate and comprehensive scientific investigations within the NEON ecosystem.

As LiDAR sensors become more precise and widely used, effectively managing and rendering large amounts of point data is becoming increasingly difficult. Available web based solutions for large scale point cloud visualization do not take advantage of modern cloud optimized data formats which are well suited for parallel and efficient data streaming. Furthermore, current web visualization tools do not take advantage of new capabilities like persistent file caching, losing their data when a tab/window is closed. In this work, we present the first web based viewer for point cloud data using a multi-layer cache system that allows to store data using both file and memory storage directly from a browser. Furthermore, we also introduce the first open source viewer for the cloud optimized point cloud (COPC) data format employing a parallel workflow using WebGPU to stream, process and render point cloud data. Experimental studies of the multi-layer cache system demonstrates to provide the best performance in different configurations, including offline data visualization.