AUGMENTATION DES RÉSERVES DE CAPACITÉ DU MÉTRO M1 DES TL

Le métro m1 des Transports lausannois (tl) est l’axe principal de transports publics de l’Ouest-Lausannois desservant notamment les Hautes Écoles depuis le centre de Lausanne et depuis la Gare de Renens. Construit en 1991, il a contribué à la transformation de ce district limitrophe, industriel en un territoire dynamique qui monte aujourd’hui en puissance. Le parc de matériel roulant datant de 1991 et 1995 achève cet été de 2018 sa maintenance de mi-vie. Si l’on peut escompter une quinzaine d’années supplémentaires de service, il est temps de mener une réflexion sur l’avenir du m1 afin d’aboutir à un projet rationnel, défendable et exécutable à l’horizon charnière 2035. Si cet horizon présente une opportunité de changer complètement de paradigme et si des idées fusent pour une métamorphose de la ligne en un autre moyen de transport, cette étude recherche des leviers d’absorption de la demande afin de pérenniser le système actuel. En effet, la demande sur le m1 ne cesse d’augmenter avec une progression attendue de vingt à quarante pourcents à l’horizon 2035. Ce rapport analyse et combine trois leviers différents pour évaluer les réserves de capacité supplémentaires réalisables jusqu’à cet horizon mais aussi à long-terme. L’implémentation de ces moyens d’action ainsi que les conséquences contingentes ont également été étudiées

FLOOD-WATER LEVEL ESTIMATION FROM SOCIAL MEDIA IMAGES

In the event of a flood, being able to build accurate flood level maps is essential for supporting emergency plan operations. In order to build such maps, it is important to collect observations from the disaster area. Social media platforms can be useful sources of information in this case, as people located in the flood area tend to share text and pictures depicting the current situation. Developing an effective and fully automatized method able to retrieve data from social media and extract useful information in real-time is crucial for a quick and proper response to these catastrophic events. In this paper, we propose a method to quantify flood-water from images gathered from social media. If no prior information about the zone where the picture was taken is available, one possible way to estimate the flood level consists of assessing how much the objects appearing in the image are submerged in water. There are various factors that make this task difficult: i) the precise size of the objects appearing in the image might not be known; ii) flood-water appearing in different zones of the image scene might have different height; iii) objects may be only partially visible as they can be submerged in water. In order to solve these problems, we propose a method that first locates selected classes of objects whose sizes are approximately known, then, it leverages this property to estimate the water level. To prove the validity of this approach, we first build a flood-water image dataset, then we use it to train a deep learning model. We finally show the ability of our trained model to recognize objects and at the same time predict correctly flood-water level

Improving merge methods for grid-based digital elevation models

Digital Elevation Models (DEMs) are used to represent the terrain in applications such as, for example, overland flow modelling or viewshed analysis. DEMs generated from digitising contour lines or obtained by LiDAR or satellite data are now widely available. However, in some cases, the area of study is covered by more than one of the available elevation data sets. In these cases the relevant DEMs may need to be merged. The merged DEM must retain the most accurate elevation information available while generating consistent slopes and aspects. In this paper we present a thorough analysis of three conventional grid-based DEM merging methods that are available in commercial GIS software. These methods are evaluated for their applicability in merging DEMs and, based on evaluation results, a method for improving the merging of grid-based DEMs is proposed. DEMs generated by the proposed method, called Id:Blend, showed significant improvements when compared to DEMs produced by the three conventional methods in terms of elevation, slope and aspect accuracy, ensuring also smooth elevation transitions between the original DEMs. The results produced by the improved method are highly relevant different applications in terrain analysis, e.g., visibility, or spotting irregularities in landforms and for modelling terrain phenomena, such as overland flow

Internet of Things for Sustainability: Perspectives in Privacy, Cybersecurity, and Future Trends

In the sustainability IoT, the cybersecurity risks to things, sensors, and monitoring systems are distinct from the conventional networking systems in many aspects. The interaction of sustainability IoT with the physical world phenomena (e.g., weather, climate, water, and oceans) is mostly not found in the modern information technology systems. Accordingly, actuation, the ability of these devices to make changes in real world based on sensing and monitoring, requires special consideration in terms of privacy and security. Moreover, the energy efficiency, safety, power, performance requirements of these device distinguish them from conventional computers systems. In this chapter, the cybersecurity approaches towards sustainability IoT are discussed in detail. The sustainability IoT risk categorization, risk mitigation goals, and implementation aspects are analyzed. The openness paradox and data dichotomy between privacy and sharing is analyzed. Accordingly, the IoT technology and security standard developments activities are highlighted. The perspectives on opportunities and challenges in IoT for sustainability are given. Finally, the chapter concludes with a discussion of sustainability IoT cybersecurity case studies

Innovating Out of the Fishmeal Trap - A case study on how niche conditions in the Norwegian aquafeed sector led to the development of a sustainable technology with global potential

As trends in population growth, resource scarcity, and global warming converge, global food security is becoming a pressing issue. To address this challenge, agricultural systems will have to transition towards more efficient and sustainable production regimes through the generation and diffusion of new technologies. The role of governance in catalyzing this change has long been a topic of research and debate. In this thesis, the author borrows from the concepts of “niches” and functional analysis to evaluate the role of governance and other factors in pioneering “Dynamic Formulation” technology for salmon within the Norwegian aquaculture sector. This technology was used to substitute expensive fishmeal, extracted from declining stocks of wild fish, with more economic and sustainable feed ingredients. It was found that the “niche” conditions, which facilitated this technological emergence, were only indirectly attributable to government policy. Instead, long-term price incentives, industry dynamics, and attributes of the technology itself were major drivers behind this innovation. This thesis contributes to multi-level and innovation system literature with a hybrid framework applied to a previously unstudied case and encourages discussion about the role of governance in optimizing an already functional “niche”.MSc in Innovation and Industrial Managemen

Public Surveillance and the Future of Urban Pluvial Flood Modelling

Motivation and objective Urban pluvial flooding is an issue of increasingly critical importance due to climate change and urbanization. However, the numerical models used for flood forecasting and risk mitigation suffer from a pronounced lack of monitoring data, which affects model accuracy. Monitoring data are necessary so the models, which contain undefined parameters, can be calibrated and validated against real flood events. In particular, it is important that the models are able to reproduce flood behavior in and around buildings, where the most damage is caused. However, conventional flow and water level sensors reach their limits in public spaces like streets due to irregular topography, moving obstacles, and the risk of vandalism. It has been suggested that surveillance cameras and social media could provide the necessary surface flooding data at a fraction of the cost of conventional sensors. The objective of this thesis is to explore how trend-like data can be extracted from surveillance footage and assimilated to boost the reliability of urban pluvial flood models. Methodology and outcome The first deliverable of this thesis addresses the general lack of data necessary for researching novel, video-based monitoring methods. For this, the floodX data sets were created by conducting realistic flooding experiments at a special flood training facility, where conventional sensors and surveillance cameras collected data in parallel. Besides supporting multiple research projects, including the methods developed and tested in this thesis, the rapid publishing of the data pushes the bar for Open Research in the field of urban water management. The second main deliverable of the thesis is a method (SOFI) to automatically obtain trend-like flooding data from surveillance cameras with the help of a convolutional neural network. The SOFI method is fully automatic, which will be necessary for deployment due to privacy concerns and data volume challenges. Despite the limited number of images used, it was possible to train a deep convolutional neural network to detect floodwater in a range of flooding situations. By performing an analysis every few seconds, trend-like water level data was obtained that had, on average, a correlation of 75% with the actual water level. While the SOFI method suffers if image quality is poor or if large obstructions block the view of the water, it has the advantage of being applicable to footage without the need for on-site surveys. In the third part of this thesis, practical value of trend-like data for improving a flood model’s predictive performance was assessed under a variety of data qualities and sensor network layouts in the floodX case study. The results indicate that error-free trend-like data can be nearly as good as - and sometimes better than - sensors when it comes to improving model performance. However, this result only holds for trend-like data with uncorrelated errors or no errors at all. Correlated errors in the trend-like data reduced the improvement achieved and in some cases, the use of erroneous data even worsened model performance. Based on the results, models calibrated with trend-like SOFI data still need to be cross-validated to ensure that any errors present in the data are not compromising predictive performance. Impact Although public surveillance is controversial from a social perspective, this thesis suggests that surveillance cameras could be used as a cost-effective data source to enhance the reliability of urban pluvial flood models, for the benefit of flood risk mitigation. For cities rushing to adapt to a changing climate with more intense rainfall, trend-like data obtained from surveillance footage, uncalibrated sensors, or other sources remain a promising alternative to costly conventional sensor data

floodX: urban flash flood experiments monitored with conventional and alternative sensors

The data sets described in this paper provide a basis for developing and testing new methods for monitoring and modelling urban pluvial flash floods. Pluvial flash floods are a growing hazard to property and inhabitants' well-being in urban areas. However, the lack of appropriate data collection methods is often cited as an impediment for reliable flood modelling, thereby hindering the improvement of flood risk mapping and early warning systems. The potential of surveillance infrastructure and social media is starting to draw attention for this purpose. In the floodX project, 22 controlled urban flash floods were generated in a flood response training facility and monitored with state-of-the-art sensors as well as standard surveillance cameras. With these data, it is possible to explore the use of video data and computer vision for urban flood monitoring and modelling. The floodX project stands out as the largest documented flood experiment of its kind, providing both conventional measurements and video data in parallel and at high temporal resolution. The data set used in this paper is available at https://doi.org/10.5281/zenodo.830513

Training images for sewer inlet detector

<p>This package consists of a zipped directory with positive and negative image samples of sewer inlets. The samples can be used to train computer vision classification algorithms.</p

New draft item

images of sensor

floodX: urban flash flood experiments monitored with conventional and alternative sensors

ISSN:1866-3516ISSN:1866-350