Light Detection and Ranging (Lidar) is a remote sensing method used to produce 3D terrain maps. Lidar scans are expensive and time consuming, sometimes requiring several passes of a scene to obtain acceptable data fidelity. For a low number of scene passes lidar data is primarily corrupted by shot noise. It is desirable to filter the shot noise to increase data efficiency. In this talk I will discuss the basic operation of lidar systems and the work I did at the Geospatial Research Laboratory this past summer in removing shot noise from Lidar data.

In collaboration with academic, industrial, and other government laboratory partners, Lawrence Livermore National Laboratory’s Center for Applied Scientific Computing (CASC) conducts world-class scientific research and development on problems in computer science, computational physics, applied mathematics, and data science. CASC applies the power of high-performance computing and the efficiency of modern computational methods to the realms of stockpile stewardship, cyber and energy security, knowledge discovery for intelligence applications, and basic scientific discovery. As the focus for research efforts in the Computation Directorate, CASC also leads the development of methods and techniques that advance the discipline of scientific computing. In CASC, we are developing the methods and tools that will enable the routine use of dynamically adjustable precision at a per-bit level depending on the needs of the task at hand. We typically compute and store simulation results in 64-bit double precision by default, even when very few significant digits are meaningful. Many of these bits represent errors – truncation, iteration, roundoff – instead of useful information about the computed solution. This over-allocation of resources is wasteful of power, bandwidth, storage, and operations. Just as adaptive mesh refinement frameworks adapt spatial grid resolution to the needs of the underlying solution, our goal is to provide more or less precision as needed locally. Acceptance from the community will require that we address three concerns: that we can ensure accuracy, ensure efficiency, and ensure ease of use in development, debugging, and application. In this talk, I will present an overview of CASC and then take a deeper dive into the benefits and the challenges of variable precision computing, highlighting aspects of our ongoing research in data representations, numerical algorithms, and testing and development tools.