Space colonisation based procedural road generation

Dissertação de mestrado em Computer ScienceProcedural generation of content has been studied for quite some time and it is increasingly relevant in scientific areas and in video-game and film industries. Procedural road layout generation has been traditionally approached using L-Systems, with some works exploring alternative avenues. Although originally conceived for biological systems modelling, the adequacy of L-Systems as a base for road generation has been demonstrated in several works. In this context, this work presents an alternative approach for procedural road layout generation that is also inspired by plant generation algorithms: space colonisation. In particular, this work uses the concept of attraction points introduced in space colonisation as its base to produce road layouts, both in urban and inter-city environments. As will be shown, the usage of attraction points provides an intuitive way to parameterise a road layout. The original Space Colonization Algorithm (SCA) generates a tree like structure, but in this work, the extensions made aim to fully generate a inter-connected road network. As most previous methods the method has two phases. A first phase generates what is mostly a tree structure growing from user defined road segments. The second phase performs the inter connectivity among the roads created in the first phase. The original SCA parameters such as the killradius help to control the capillarity of the road layout, the number of attraction points used by each segment will dictate its relevance establishing a road hierarchy naturally dependent on the distribution of the attraction points on the terrain. An angle control allows the creation of grid like or more organic road layouts. The distribution of the attraction points in the terrain can be conditioned by boundary maps, containing parks, sea, rivers, and other forbidden areas. Population density maps can be used to supply an explicit probabilistic distribution to the attraction points. Flow-fields can be used to dictate the flow of the road layout. Elevation maps provide an additional restriction regarding the steepness of the roads. The tests were executed within a graphic toolbox developed simultaneously. The results are exported to a geographical information file format, GeoJSON, and then maps are rendered using a geospatial visualisation and processing framework called Mapnik. For the most part, parameter settings were intuitively reflected on the road layout and this method can be seen as a first step towards fully exploring the usage of attraction points in the context of road layout.Gradualmente a geração procedimental de conteúdo tem-se tornado cada vez mais relevante, sendo maioritariamente aplicada em industrias como a dos vídeo-jogos e cinema. No que toca à geração procedimental de redes de estradas, grande parte das abordagens em torno deste tema são baseadas em L-Systems. Embora a área de aplicação dos L-Systems tenha sido originalmente para produzir modelos de sistemas biológicos, mostrou também ser um algoritmo adequado para a geração procedimental de redes de estradas. Este trabalho apresenta uma abordagem alternativa à geração procedimental de redes de estradas que também é inspirada num algoritmo procedimental de geração de plantas, colonização espacial, utilizando o conceito de pontos de atracão como base para gerar padrões de estradas. Como será demonstrado, a utilização de pontos de atracão fornece uma maneira intuitiva de parametrizar um padrão de estradas desejado. Como a maioria dos trabalhos feitos nesta área, este método tem duas fases. A primeira fase gera uma rede semelhante a uma árvore criada a partir de um ou mais segmentos iniciais da rede determinados pelo utilizador. A segunda fase trata de interligar as estradas geradas na primeira fase. Os parâmetros iniciais do algoritmo de colonização espacial, como o kill radius, ajudam a controlar a capilaridade da rede, os pontos de atracão que influenciam cada segmento irão ditar a sua relevância na rede geral, estabelecendo a noção de hierarquia de estradas, dependendo da distribuição de pontos de atracão no terreno. O controlo do ângulo entre segmentos permite a criação de padrões de estradas tanto em forma de grelha como padrões mais orgânicos. A distribuição dos pontos de atracão no terreno pode ser influenciada por mapas de fronteira, que contem as áreas válidas e/ou inválidas, como parques, mar, rios, e outras áreas proibidas. Mapas de densidade populacional podem ser usados para fornecer uma distribuição probabilística dos pontos de atracão. Campos de forças, podem ser usados para ditar o fluxo da rede de estradas. Mapas de elevação oferecem uma restrição adicional tendo em conta a inclinação das estradas. De um modo geral, as definições de parâmetros refletiram-se de um modo intuitivo nos padrões de redes de estradas gerados, e este trabalho pode ser considerado como um primeiro passo na exploração do conceito de pontos de atracão na área da geração de redes de estradas

Semi-Automated DIRSIG scene modeling from 3D lidar and passive imagery

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multispectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for multi-sensor algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG’s ability to generate scenes ”on demand.” To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, this research developed an approach to reduce the man-in-the-loop requirements for several aspects of synthetic scene construction. Through a fusion of 3D lidar data with passive imagery, we were able to semi-automate several of the required tasks in the DIRSIG scene creation process. Additionally, many of the remaining tasks realized a shortened implementation time through this application of multi-modal imagery. Lidar data is exploited to identify ground and object features as well as to define initial tree location and building parameter estimates. These estimates are then refined by analyzing high-resolution frame array imagery using the concepts of projective geometry in lieu of the more common Euclidean approach found in most traditional photogrammetric references. Spectral imagery is also used to assign material characteristics to the modeled geometric objects. This is achieved through a modified atmospheric compensation applied to raw hyperspectral imagery. These techniques have been successfully applied to imagery collected over the RIT campus and the greater Rochester area. The data used include multiple-return point information provided by an Optech lidar linescanning sensor, multispectral frame array imagery from the Wildfire Airborne Sensor Program (WASP) and WASP-lite sensors, and hyperspectral data from the Modular Imaging Spectrometer Instrument (MISI) and the COMPact Airborne Spectral Sensor (COMPASS). Information from these image sources was fused and processed using the semi-automated approach to provide the DIRSIG input files used to define a synthetic scene. When compared to the standard manual process for creating these files, we achieved approximately a tenfold increase in speed, as well as a significant increase in geometric accuracy

Argoverse: 3D Tracking and Forecasting with Rich Maps

We present Argoverse -- two datasets designed to support autonomous vehicle machine learning tasks such as 3D tracking and motion forecasting. Argoverse was collected by a fleet of autonomous vehicles in Pittsburgh and Miami. The Argoverse 3D Tracking dataset includes 360 degree images from 7 cameras with overlapping fields of view, 3D point clouds from long range LiDAR, 6-DOF pose, and 3D track annotations. Notably, it is the only modern AV dataset that provides forward-facing stereo imagery. The Argoverse Motion Forecasting dataset includes more than 300,000 5-second tracked scenarios with a particular vehicle identified for trajectory forecasting. Argoverse is the first autonomous vehicle dataset to include "HD maps" with 290 km of mapped lanes with geometric and semantic metadata. All data is released under a Creative Commons license at www.argoverse.org. In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting. Our tracking and forecasting experiments represent only an initial exploration of the use of rich maps in robotic perception. We hope that Argoverse will enable the research community to explore these problems in greater depth.Comment: CVPR 201

Spatial ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes

Multispectral Image Road Extraction Based Upon Automated Map Conflation

Road network extraction from remotely sensed imagery enables many important and diverse applications such as vehicle tracking, drone navigation, and intelligent transportation studies. There are, however, a number of challenges to road detection from an image. Road pavement material, width, direction, and topology vary across a scene. Complete or partial occlusions caused by nearby buildings, trees, and the shadows cast by them, make maintaining road connectivity difficult. The problems posed by occlusions are exacerbated with the increasing use of oblique imagery from aerial and satellite platforms. Further, common objects such as rooftops and parking lots are made of materials similar or identical to road pavements. This problem of common materials is a classic case of a single land cover material existing for different land use scenarios. This work addresses these problems in road extraction from geo-referenced imagery by leveraging the OpenStreetMap digital road map to guide image-based road extraction. The crowd-sourced cartography has the advantages of worldwide coverage that is constantly updated. The derived road vectors follow only roads and so can serve to guide image-based road extraction with minimal confusion from occlusions and changes in road material. On the other hand, the vector road map has no information on road widths and misalignments between the vector map and the geo-referenced image are small but nonsystematic. Properly correcting misalignment between two geospatial datasets, also known as map conflation, is an essential step. A generic framework requiring minimal human intervention is described for multispectral image road extraction and automatic road map conflation. The approach relies on the road feature generation of a binary mask and a corresponding curvilinear image. A method for generating the binary road mask from the image by applying a spectral measure is presented. The spectral measure, called anisotropy-tunable distance (ATD), differs from conventional measures and is created to account for both changes of spectral direction and spectral magnitude in a unified fashion. The ATD measure is particularly suitable for differentiating urban targets such as roads and building rooftops. The curvilinear image provides estimates of the width and orientation of potential road segments. Road vectors derived from OpenStreetMap are then conflated to image road features by applying junction matching and intermediate point matching, followed by refinement with mean-shift clustering and morphological processing to produce a road mask with piecewise width estimates. The proposed approach is tested on a set of challenging, large, and diverse image data sets and the performance accuracy is assessed. The method is effective for road detection and width estimation of roads, even in challenging scenarios when extensive occlusion occurs

Automatic High-Fidelity 3D Road Network Modeling

Many computer applications such as racing games and driving simulations frequently make use of 3D high-fidelity road network models for a variety of purposes. However, there are very few existing methods for automatic generation of 3D realistic road networks, especially for those in the real world. On the other hand, vast road network GIS data have been collected in the past and used by a wide range of applications, such as navigation and evaluation. A method that can automatically produce 3D high-fidelity road network models from 2D real road GIS data will significantly reduce both the labor and time needed to generate these models, and greatly benefit numerous applications involving road networks. Based on a set of selected civil engineering rules for road design, this dissertation research addresses this problem with a novel approach which transforms existing road GIS data that contain only 2D road centerline information into 3D road network models. The proposed method consists of several components, mainly including road GIS data preprocessing, 3D centerline modeling and 3D geometry modeling. During road data preprocessing, topology of the road network is extracted from raw road data as a graph composed of road nodes and road links; road link information is simplified and classified. In the 3D centerline modeling part, the missing height information of the road centerline is inferred based on 2D road GIS data, intersections are extracted from road nodes and the whole road network is represented as road intersections and road segments in parametric forms. Finally, the 3D road centerline models are converted into various 3D road geometry models consisting of triangles and textures in the 3D geometry modeling phase. With this approach, basic road elements such as road segments, road intersections and traffic interchanges are generated automatically to compose sophisticated road networks. Results show that this approach provides a rapid and efficient 3D road modeling method for applications that have stringent requirements on high-fidelity road models

Accurate matching and reconstruction of line features from ultra high resolution stereo aerial images

In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned(<= 10 degrees) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen-Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line