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

Detecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images

A method for the automatic detection of buildings and their roof planes from LIDAR data and multispectral images is presented. For building detection, a classification technique is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. The detection of roof planes is based on a region growing algorithm applied to the LIDAR data, the seed regions detected by a grey-level segmentation of the multispectral images. We describe the algorithms involved, giving examples for a test site in Fairfield (Sydney)

Change detection of buildings from satellite imagery and lidar data

Geospatial objects change over time and this necessitates periodic updating of the cartography that represents them. Currently, this updating is done manually, by interpreting aerial photographs, but this is an expensive and time-consuming process. While several kinds of geospatial objects are recognized, this article focuses on buildings. Specifically, we propose a novel automatic approach for detecting buildings that uses satellite imagery and laser scanner data as a tool for updating buildings for a vector geospatial database. We apply the support vector machine (SVM) classification algorithm to a joint satellite and laser data set for the extraction of buildings. SVM training is automatically carried out from the vector geospatial database. For visualization purposes, the changes are presented using a variation of the traffic-light map. The different colours assist human operators in performing the final cartographic updating. Most of the important changes were detected by the proposed method. The method not only detects changes, but also identifies inaccuracies in the cartography of the vector database. Small houses and low buildings surrounded by high trees present significant problems with regard to automatic detection compared to large houses and taller buildings. In addition to visual evaluation, this study was checked for completeness and correctness using numerical evaluation and receiver operating characteristic curves. The high values obtained for these parameters confirmed the efficacy of the method

Writ(h)ing Images: Imagination, the Human Form, and the Divine in William Blake, Salman Rushdie, and Simon Louvish

In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. The LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. These features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. The test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters

Linear Feature Extraction of Buildings from Terrestrial LIDAR Data with Morphological Techniques

LiDAR has been a major interest of photogrammetry to acquire three dimensional objects. It has shown its promising capabilities in building virtual reality applications, such as virtual campus and virtual historic sites. However, point clouds of LiDAR data always occupy a large sum of storage capacity. This blocks further fast processing of LiDAR data to combine with GIS to build virtual reality. The research focused on linear feature extraction of buildings from terrestrial LiDAR data. To obtain linear features of buildings is one of the critical steps to realize minimization of redundant data and high efficiency of data processing. The paper discussed the procedure of linear features extracting of buildings and mainly put forward edge detection algorithms based on fractal dimension theory. Triangular method was chosen to obtain fractal dimension values of grids. The algorithm was not only effective and efficient to detect building edges, but also helpful for segmenting the building and nature objects. Future work was also discussed in the end

Linear Feature Extraction of Buildings from Terrestrial LIDAR Data with Morphological Techniques

LiDAR has been a major interest of photogrammetry to acquire three dimensional objects. It has shown its promising capabilities in building virtual reality applications, such as virtual campus and virtual historic sites. However, point clouds of LiDAR data always occupy a large sum of storage capacity. This blocks further fast processing of LiDAR data to combine with GIS to build virtual reality. The research focused on linear feature extraction of buildings from terrestrial LiDAR data. To obtain linear features of buildings is one of the critical steps to realize minimization of redundant data and high efficiency of data processing. The paper discussed the procedure of linear features extracting of buildings and mainly put forward edge detection algorithms based on fractal dimension theory. Triangular method was chosen to obtain fractal dimension values of grids. The algorithm was not only effective and efficient to detect building edges, but also helpful for segmenting the building and nature objects. Future work was also discussed in the end

Building Detection Using LIDAR Data and Multispectral Images

A method the automatic detection of buildings from LIDAR data and multispectral images is presented. A classification technique using various cues derived from these data is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. Both first and last pulse data and the normalised difference vegetation index are used in that process. We describe the algorithms involved, giving examples for a test site in Fairfield (Victoria)

FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur

Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method

This article describes a method to model roof geometries from widely available low-resolution (2 m horizontal) Light Detection and Ranging (LiDAR) datasets for application on a city wide scale. The model provides roof area, orientation, and slope, appropriate for predictions of solar technology performance, being of value to national and regional policy makers in addition to investors and individuals appraising the viability of specific sites. Where present, similar buildings are grouped together based on proximity and building footprint dimensions. LiDAR data from all the buildings in a group is combined to construct a shared high-resolution LiDAR dataset. The best-fit roof shape is then selected from a catalogue of common roof shapes and assigned to all buildings in that group. Method validation was completed by comparing the model output to a ground-based survey of 169 buildings and aerial photographs of 536 buildings, all located in Leeds, UK. The method correctly identifies roof shape in 87% of cases and the modelled roof slope has a mean absolute error of 3.76°. These performance figures are only possible when segmentation, similar building grouping and ridge repositioning algorithms are used

SPATIAL DISTRIBUTION REQUIREMENTS OF REFERENCE GROUND CONTROL FOR ESTIMATING LIDAR/INS BORESIGHT MISALIGNMENT

LiDAR (Light Detection and Ranging, also known as Airborne Laser Scanning – ALS) is a powerful technology for obtaining detailed and accurate terrain models as well as precise description of natural and man-made objects from airborne platforms, with excellent vertical accuracy. High performance integrated GPS/INS systems provide the necessary navigation information for the LiDAR data acquisition platform, and therefore, the proper calibration of the entire Mobile Mapping System (MMS) including individual and inter-sensor calibration, is essential to determine the accurate spatial  relationship of the involved sensors. In particular, the spatial relationship between the INS body frame and the LiDAR body frame is of high importance as it could be the largest source of systematic errors in airborne MMS. The feasibility of using urban areas, especially buildings, for boresight misalignment is still investigated. In this research, regularly or randomly distributed, photogrammetrically restituted buildings are used as reference surfaces, to investigate the impact of  the spatial distribution and the distance between the necessary ‘building-positions’ on boresight’s misalignment parameter estimation. The data used for performance evaluation included LiDAR point clouds Pothou, A. et al  777 and aerial images captured in a test area in London, Ohio, USA. The city includes mainly residential houses and a few bigger buildings