OpenStreetMap

Résumé

OpenStreetMap (OSM) est un projet collaboratif de cartographie en ligne qui vise à constituer une base de données géographiques libre du monde (permettant par exemple de créer des cartes sous licence libre), en utilisant le système GPS et d'autres données libres. Il est mis en route en par Steve Coast à l'University College de Londres. Le projet a été inspiré par le succès de Wikipédia et la prédominance des données cartographiques propriétaires au Royaume-Uni et dans d'autres pays. Depuis, il a atteint plus de dix millions d'utilisateurs enregistrés. Les utilisateurs peuvent collecter des données à l'aide de relevés manuels, de GPS, de photographies aériennes, d'autres sources gratuites, ou utiliser leur propre connaissance de la région. Ces données issues de la production participative sont ensuite mises à disposition sous la licence Open Database License. Les données d'OSM peuvent être utilisées de diverses manières, notamment pour produire des cartes papier et des cartes électr

Source officielle

https://fr.wikipedia.org/wiki/OpenStreetMap

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Concepts associés (29)

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Cours associés (7)

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Séances de cours associées (11)

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This paper presents a methodology that use volunteered geographic information (VGI), cyclist GPS tracking and Open Street Map network, with network based kernel density estimation. It investigates optimal location for cycle paths and lanes development. Recently completed research provides cycling data for Ljubljana, Slovenia. It was conducted over two years and was commissioned by the Municipality of Ljubljana. The methodology combines and adapts these VGI data and is mainly based on open source software. It handles large datasets with multiscale perspectives. This methodology should help planners to find and to develop suitable facility locations corresponding to current user behaviors.

Springer-Verlag Berlin2011

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In a time of rising concern about climate change and pollution, the water quality of large lakes acts as an indicator of the health of the environment. To study the water quality at a large scale - up to several hundreds of kilometres - hyperspectral remote sensing is emerging as the main solution. Indeed, different quantities relevant to water quality, like turbidity or concentratrion in chlorophyll-a, can be measured using the spectral reflectance of the water column. Additionally, airborne and spaceborne sensors can cover large areas, thus allowing to study the water at a much larger scale than when simply taking water samples at specific points. Airborne hyperspectral imaging, in particular, offers an acceptable ground resolution - around a metre - which allows to map relevant quantities precisely. However, few existing projects deliver maps that have both a sufficient ground resolution and a large coverage. Furthermore, most existing sensors do not offer a fine spectral resolution, which is for instance crucial when studying the presence of chlorophyll-a, which can only be detected in a narrow range of the electromagnetic spectrum. This thesis presents our work with a hyperspectral sensor developed and used by the Geodetic Engineering Laboratory of EPFL in the Léman-Baïkal project, a cooperative work which aimed at studying both Lake Geneva (Switzerland) and Lake Baikal (Russia). The project included ultralight plane flights with an onboard pushbroom scanner, which allowed to collect data over large areas with a fine spectral resolution. Alongside the use of this sensor came problematics which are at the centre of this thesis: the georeferencing of the scan lines, their radiometric calibration, their analysis and the softwaremanagement of this data. In the following, we present a new method to georeference pushbroom scan lines that uses co-acquired frame images to perform coregistration and to achieve a georeferencing, which RMSE is up to 20 times smaller than the direct one. We propose an efficient radiometric self-calibration method to convert the sensor output to water-leaving reflectance; this method makes use of the visible peaks of atmospheric absorption to align the spectral bands with those of a reference acquisition, and uses the near infrared properties of deep water and vegetation to performabsolute calibration. The last part of the processing - the software management, including data compression - was solved by developing a software called HYPerspectral Orthorectification Software (HypOS). This software is the synthesis of our work, including the tools to performgeometric correction, radiometric calibration and data compression of our hyperspectral data. Two examples of applications are given: the first one deals with mapping chlorophyll-a in the Rhone Delta of Lake Geneva; the second, at a larger scale, uses satellite data to monitor ice coverage over large lakes like Onega or Ladoga (Russia).

EPFL2018

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Learning Aerial Image Segmentation from Online Maps

Thomas Hofmann, Martin Jaggi, Aurélien Lucchi

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) have shown impressive performance and have quickly become the de-facto standard for semantic segmentation, with the added benefit that task-specific feature design is no longer necessary. However, a major downside of deep learning methods is that they are extremely data-hungry, thus aggravating the perennial bottleneck of supervised classification, to obtain enough annotated training data. On the other hand, it has been observed that they are rather robust against noise in the training labels. This opens up the intriguing possibility to avoid annotating huge amounts of training data, and instead train the classifier from existing legacy data or crowd-sourced maps which can exhibit high levels of noise. The question addressed in this paper is: can training with large-scale, publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance? Such data will inevitably contain a significant portion of errors, but in return virtually unlimited quantities of it are available in larger parts of the world. We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled, pixel-accurate ground truth of the same city to automatic training data derived from OpenStreetMap data from distant locations. We report our results that indicate that satisfying performance can be obtained with significantly less manual annotation effort, by exploiting noisy large-scale training data.

Institute of Electrical and Electronics Engineers2017

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EPFL2018