Developing an acceptance test for non-hydrographic airborne bathymetric lidar data application to NOAA charts in shallow waters

Hydrographic data of the National Oceanic and Atmospheric Administration are typically acquired using sonar systems, with a small percent acquired via airborne lidar bathymetry for nearshore areas. This study investigates an integrated approach to meeting NOAA’s hydrographic survey requirements for nearshore areas of NOAA charts using existing U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) topographic-bathymetric lidar (TBL) data. Because these existing NCMP bathymetric lidar datasets were not collected to NOAA hydrographic surveying standards, it is unclear if, and under what circumstances, they might aid in meeting certain hydrographic surveying requirements. The NCMP bathymetric lidar data were evaluated through a comparison against NOAA’s hydrographic Services Division (HSD) data derived from acoustic surveys. Key goals included assessing whether NCMP bathymetry can be used to fill in the data gap shoreward of the navigable area limit line (0 to 4 m depth) and if there is potential for applying NCMP TBL data to nearshore areas deeper than 10 m. The study results were used to make recommendations for future use of the data in NOAA. Additionally, this work may allow the development of future operating procedures and workflows using other topographicbathymetric lidar datasets to help update nearshore areas of the NOAA charts

Deep Generative Modeling of LiDAR Data

Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201

Theoretical Lidar Point Density for Topographic Mapping in the Largest Scales

When ordering LiDAR data, LiDAR point density per surface unit is important information with decisive influence on the price of the LiDAR survey. The paper first deals with the theoretical calculation of the minimum LiDAR point density, necessary for the acquisition of topographic data of the largest scales. For this purpose the sampling theorem is used. However, since topographic objects (roads, water bodies, etc.) and phenomena represented on topographic maps and in topographic bases are in many cases located under vegetation, also the rate of laser beam penetration through vegetation for the area where the topographic data are to be gathered has to be known. In a research on a test case conducted in the area of the town Nova Gorica we calculated the rate of laser beam penetration for four different vegetation types: scarce Mediterranean vegetation, thick thermophilic deciduous forest, mixed vegetation (meadows, orchards and forest) and built-up area. By connecting the theoretic minimum LiDAR point density with the rate of penetration, we defined the minimum LiDAR point density for the needs of data acquisition on topographic maps of the largest scales or in topographic bases of comparable detail (from 1 : 1000 to 1 : 10,000)

Riparian vegetation classification from airborne laser scanning data with an emphasis on cottonwood trees

The high point density of airborne laser mapping systems enables achieving a detailed description of geographic objects and the terrain. Growing experience indicates, however, that extracting useful information directly from the data can be difficult. In this study, small-footprint lidar data were used to differentiate between young, mature, and old cottonwood trees in the San Pedro River Basin near Benson, Arizona, USA. The lidar data were acquired in June 2003, using the Optech Incorporated ALTM 1233 (Optech Incorporated, Toronto, Ont.), during flyovers conducted at an altitude of 750 m. The lidar data were preprocessed to create a two-band image of the study site: a high-accuracy canopy altitude model band, and a near-infrared intensity band. These lidar-derived images provided the basis for supervised classification of cottonwood age categories, using a maximum likelihood algorithm. The results of classification illustrate the potential of airborne lidar data to differentiate age classes of cottonwood trees for riparian areas quickly and accurately. © 2006, Taylor & Francis Group, LLC. All rights reserved

Mesh-based 3D Textured Urban Mapping

In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context. Successful mapping algorithms have been proposed in the last decade building the map leveraging on data from a single sensor. The focus of the system presented in this paper is twofold: the joint estimation of a 3D map from lidar data and images, based on a 3D mesh, and its texturing. Indeed, even if most surveying vehicles for mapping are endowed by cameras and lidar, existing mapping algorithms usually rely on either images or lidar data; moreover both image-based and lidar-based systems often represent the map as a point cloud, while a continuous textured mesh representation would be useful for visualization and navigation purposes. In the proposed framework, we join the accuracy of the 3D lidar data, and the dense information and appearance carried by the images, in estimating a visibility consistent map upon the lidar measurements, and refining it photometrically through the acquired images. We evaluate the proposed framework against the KITTI dataset and we show the performance improvement with respect to two state of the art urban mapping algorithms, and two widely used surface reconstruction algorithms in Computer Graphics.Comment: accepted at iros 201

Exploiting Full-Waveform Lidar Data and Multiresolution Wavelet Analysis for Vertical Object Detection and Recognition

A current challenge in performing airport obstruction surveys using airborne lidar is lack of reliable, automated methods for extracting and attributing vertical objects from the lidar data. This paper presents a new approach to solving this problem, taking advantage of the additional data provided byfull-waveform systems. The procedure entails first deconvolving and georeferencing the lidar waveformdata to create dense, detailed point clouds in which the vertical structure of objects, such as trees, towers, and buildings, is well characterized. The point clouds are then voxelized to produce high-resolution volumes of lidar intensity values, and a 3D wavelet decomposition is computed. Verticalobject detection and recognition is performed in the wavelet domain using a multiresolution template matching approach. The method was tested using lidar waveform data and ground truth collected for project areas in Madison,Wisconsin. Preliminary results demonstrate the potential of the approach

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented