Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

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

Road Surface Feature Extraction and Reconstruction of Laser Point Clouds for Urban Environment

Automakers are developing end-to-end three-dimensional (3D) mapping system for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AVs). Using geomatics, artificial intelligence, and SLAM (Simultaneous Localization and Mapping) systems to handle all stages of map creation, sensor calibration and alignment. It is crucial to have a system highly accurate and efficient as it is an essential part of vehicle controls. Such mapping requires significant resources to acquire geographic information (GIS and GPS), optical laser and radar spectroscopy, Lidar, and 3D modeling applications in order to extract roadway features (e.g., lane markings, traffic signs, road-edges) detailed enough to construct a “base map”. To keep this map current, it is necessary to update changes due to occurring events such as construction changes, traffic patterns, or growth of vegetation. The information of the road play a very important factor in road traffic safety and it is essential for for guiding autonomous vehicles (AVs), and prediction of upcoming road situations within AVs. The data size of the map is extensive due to the level of information provided with different sensor modalities for that reason a data optimization and extraction from three-dimensional (3D) mobile laser scanning (MLS) point clouds is presented in this thesis. The research shows the proposed hybrid filter configuration together with the dynamic developed mechanism provides significant reduction of the point cloud data with reduced computational or size constraints. The results obtained in this work are proven by a real-world system

Extracting Physical and Environmental Information of Irish Roads Using Airborne and Mobile Sensors

Airborne sensors including LiDAR and digital cameras are now used extensively for capturing topographical information as these are often more economical and efficient as compared to the traditional photogrammetric and land surveying techniques. Data captured using airborne sensors can be used to extract 3D information important for, inter alia, city modelling, land use classification and urban planning. According to the EU noise directive (2002/49/EC), the National Road Authority (NRA) in Ireland is responsible for generating noise models for all roads which are used by more than 8,000 vehicles per day. Accordingly, the NRA has to cover approximately 4,000 km of road, 500m on each side. These noise models have to be updated every 5 years. Important inputs to noise model are digital terrain model (DTM), 3D building data, road width, road centre line, ground surface type and noise barriers. The objective of this research was to extract these objects and topographical information using nationally available datasets acquired from the Ordnance Survey of Ireland (OSI). The OSI uses ALS50-II LiDAR and ADS40 digital sensors for capturing ground information. Both sensors rely on direct georeferencing, minimizing the need for ground control points. Before exploiting the complementary nature of both datasets for information extraction, their planimetric and vertical accuracies were evaluated using independent ground control points. A new method was also developed for registration in case of any mismatch. DSMs from LiDAR and aerial images were used to find common points to determine the parameters of 2D conformal transformation. The developed method was also evaluated by the EuroSDR in a project which involved a number of partners. These measures were taken to ensure that the inputs to the noise model were of acceptable accuracy as recommended in the report (Assessment of Exposure to Noise, 2006) by the European Working Group. A combination of image classification techniques was used to extract information by the fusion of LiDAR and aerial images. The developed method has two phases, viz. object classification and object reconstruction. Buildings and vegetation were classified based on Normalized Difference Vegetation Index (NDVI) and a normalized digital surface model (nDSM). Holes in building segments were filled by object-oriented multiresolution segmentation. Vegetation that remained amongst buildings was classified using cues obtained from LiDAR. The short comings there in were overcome by developing an additional classification cue using multiple returns. The building extents were extracted and assigned a single height value generated from LiDAR nDSM. The extracted height was verified against the ground truth data acquired using terrestrial survey techniques. Vegetation was further classified into three categories, viz. trees, hedges and tree clusters based on shape parameter (for hedges) and distance from neighbouring trees (for clusters). The ground was classified into three surface types i.e. roads and parking area, exposed surface and grass. This was done using LiDAR intensity, NDVI and nDSM. Mobile Laser Scanning (MLS) data was used to extract walls and purpose built noise barriers, since these objects were not extractable from the available airborne sensor data. Principal Component Analysis (PCA) was used to filter points belonging to such objects. A line was then fitted to these points using robust least square fitting. The developed object extraction method was tested objectively in two independent areas namely the Test Area-1 and the Test Area-2. The results were thoroughly investigated by three different accuracy assessment methods using the OSI vector data. The acceptance of any developed method for commercial applications requires completeness and correctness values of 85% and 70% respectively. Accuracy measures obtained using the developed method of object extraction recommend its applicability for noise modellin