Abstract

The automatic generation of street networks is attracting the attention of research and industry communities in areas such as routable map generation. This paper presents a novel mechanism that focuses on the automatic detection of street elements such as traffic lights, street crossings and roundabouts which could be used to generate street maps and populate them with traffic influencing infrastructural elements such as traffic lights. In order to minimize the system requirements and simplify the data collection from many users with minimal impact for them, only traces of GPS data from a mobile device while driving are used. Speed and acceleration time series are derived from the GPS data. An outlier detection algorithm is used first in order to detect abnormal driving locations (which can be due to infrastructural elements or particular traffic conditions). Using deep learning, speed and acceleration patterns are automatically analyzed at each outlier in order to extract relevant features which are then classified into a traffic light, street crossing, urban roundabout or other element. The classification results are enhanced by adding the degree of atypicity for each point calculated as the percentage of times that a particular location is detected as an outlier in several drives. The proposed algorithm achieves a combined recall of 0.89 and a combined precision of 0.88 for classification. The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R (MINECO), funded by the Spanish Agencia Estatal de Investigación (AEI), and the “ANALYTICS USING SENSOR DATA FOR FLATCITY” project TIN2016-77158-C4-1-R (MINECO/ ERDF, EU) funded by the Spanish Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (ERDF). Document type: Article

Abstract

Complex interactions existing between water distribution systems' materials and water can cause a reduction in water quality and unwanted changes in materials, aging or corrosion of materials and formation of biofilms on surfaces. Substances leaching from pipe materials and water fittings, as well as the microbiological quality of water and formation of biofilms were evaluated by applying a Living Lab theme i.e. a research in a real life setting using a full scale system during its first year of operation. The study site was a real office building with one part of the building lined with copper pipes, the other with cross-linked polyethylene (PEX) pipes thus enabling material comparison; also differences within the cold and hot water systems were analysed. It was found that operational conditions, such as flow conditions and temperature affected the amounts of metals leaching from the pipe network. In particular, brass components were considered to be a source of leaching; e. g. the lead concentration was highest during the first few weeks after the commissioning of the pipe network when the water was allowed to stagnate. Assimilable organic carbon (AOC) and microbially available phosphorus (MAP) were found to leach from PEX pipelines with minor effects on biomass of the biofilm. Cultivable and viable biomass (heterotrophic plate count (HPC), and adenosine triphosphate (ATP)) levels in biofilms were higher in the cold than in the hot water system whereas total microbial biomass (total cell count (DAPI)) was similar with both systems. The type of pipeline material was not found to greatly affect the microbial biomass or Alpha-, Beta- and Gammaproteobacteria profiles (16s rRNA gene copies) after the first one year of operation. Also microbiological quality of water was found to deteriorate due to stagnation.

Abstract

Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges.