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The way to go with WPS

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so WPS the Web Processing Service of course is the OGC standard for computational oriented of Web services has much in common with particles like the WMS and WFS what we'd support for of a synchronous request suitable for long-running processes and also wrote support for a nested requests so my presentation is mainly about a particular application we use we we as to serve the results and also an example of using nested requests would be this and then the application is it's a path planning
problem for on the way to go In other words to find the best route between 2 points were best may be the quickest the shortest the most economical with the least chance of accidents for example and usually performed on a network like this road network that is pictures here with nodes edges that connects them and weights on each edge has however but we would like
to go places like this or
this and basically anywhere you can go on foot or with the ground legal so so we want to build the
path planning application for training and initially to cover all of my and my country Norway which is about 2 thousand kilometers long and find the safest route and not restrict motion to roads of paths and estimate travel time and perhaps other things like exposure visibility and I'm going to use this small area north of Mosul 5 by 5 kilometers should examples on how we build the graph to run for and so our
perspective is that we need to do a situation-dependent path planning in a large graph situation-dependent mean that means that the weights may change of according to the time of the situation it might be a weather dependent for example seasons dependence on I also we have to relate to us service-oriented information infrastructure and that's where wp is constant I we have very detail land cover data on the type of vegetation support and we're sort of expecting in the coming years to have access to a very high resolution elevation data on a national scale so we're trying to tailor the application to have the potential that's inherent in that kind of data and also we are working on simulations of ground physics so that we can also predict things like temperature profiles and around and load carrying capacity of the ground so that's our starting point so here on
going to put the most weight on the on the graph generation show some routine examples and say a little bit about the years in the implementation
so I have to run out of which create a graph that's covers all of all of the land there are various possibilities you can have a regular graph like this perhaps with diagonals as well a very coarse grid shown here but it tends to produce a great number of nodes so you can have a random graph where the nodes are distributed more directly and there's the graph visibility graph where the nodes are usually placed close to obstacles and you assume that you can move freely on the edges between nodes connecting those nodes on the same so the Voronoi graph which is also used in robotics for example typically then the centroid in the graph in each cell is uh at an obstacle and you want to move along the edges over the cells to be as far as possible away from the obstacles however the for persons moving into rain you often find that you mean you pass obstacles as close as possible and finally both there are more but navigation measures which I believe are popular in gaming the game and straight it's also made from polygons where you assume that you can move freely within each polygon and the edges connect adjacent columns so our approach is to use
and we the random graphs but we somewhat judiciously distributed nodes in other words the nodes positions are sampled from some distribution that reflects the terrain properties so the more nodes were more nodes are needed to put so that's a graph types and so we also have some different
data types to produce the grade so elevation of course I went from elevation data we also derive attributes that describe the during more succinctly like this is what I
call actual variance which basically tells you how much your surface normal lecture jumps around in the neighborhood and other attributed and you do right as well so then there's the land cover
data I just showed here as polygons to give an impression of the detail this is a data set that's used a lot in in forestry so it describes for example the potential yield all the forest but it has the the data about soil type agitation time or whether it's a naked roads on etc. so then there is a road networks of course and path trail networks and for the latter we use that for the time being reuse OpenStreetMap data and then there's the uh that's basically what we are using for the moment we also plant implement the latter categories especially lighter ground physics and weather forecasts this is
another view of a photograph of the area that cover problems so this 3 lakes in the middle of the forest all this is
an example of a later late ly the datasets we haven't got so much of that yet so we just tested it's very exciting to work with gives a whole different Our characterization of round morphology and the vegetation
so of guiding principles then is to go use create a random graph with no density depending on during attributes we use a trying relation to form the edges but we want to our have that but have the ability to have a great number of nodes the woods arbitrary of spatial resolution and also on an arbitrary geographical scale so that's why we introduce a hierarchical representation of its of having our graph in several the levels of layers where each layer of the unknown in 1 layer is formed from a cluster of nodes in more detail later so what what
to to generate the graph the 1st lay out the uh the roads data what we serve all vertices which I used what for computing the weights but we convert some of the vertices tune to in graph nodes as well so we have extra nodes to connect to the rest of the graph we do the same with the path constant and then we distribute altering notes this
so in my move your friend here course resolution but it's search for an illustration purposes so we
generate the graph in this example we completely avoid lakes and marshes so that in the winter time of course we want to include that and just to reflect that the surface in the edge weights
are so working with gas very convenient to use represented as a sparse matrix or each of each node is a role and a column in there large matrix with many zeros and non-zero elements represent connected nodes and the diagonal reflected in the case of it's a directed graph the hierarchical
representation simple idea of merging of nearby nodes nearby in the sense of our distance measures which which is travel time in this application so that we can I start happening very on the course level and pretty 3 a shortest path algorithm on the solution at the of the union of all the nodes in the shortest path solution of the course level perhaps including neighboring neighbors of neighboring clusters so it's a it's a way of bridging been reducing the number of nodes that enters into the computation on how 1 of the reasons we do this but if you recall we we want to do but a dynamic company up the computation of the of the weights and the weights are a situation-dependent so the we cannot of update the whole a gigantic graph 1 for each other request so we start at the
very finest level of detail and we want to merge the nodes that are close in the sense of travel time and you so 1 way to view this problem is to say that we have this here we have a given cluster size they want to work on the level of a 10 minute walking distance for example and I and how can I find the the cluster distribution that requires the least number of clusters and it turns out that this question is I computationally more complex it's NP-hard so but there are approximations that thorough efficiency so so that's what we do of the soul essentially this this way of doing it requires 1 1 single source all shortest path complication per cluster where and the number of nodes that enter the each computation is determined by the variational how many how many clusters you want per units area for example so this is just an example again of clustering the notes in the previous example are shown in different colors only the 10 minute walking distance so cost functions of 1st we derive cost functions for what we call a standard conditions differentiating between roads power and terrain and the standard cost the functions are used for the generating the grade which are which is at the base of the application and then I'll for the shortest path request we we compute weights dynamically so the standard cost functions are based on literature sociology literature and more a little bit of physics and during cold data and then scale factors can be applied to account for the effect of uh same moist ground state round of snow roughness roughness tends to tends to decrease the speed with a few % etc. so that this is kind of of has to be tuned or learned ideally based on actual empirical data and we have different categories for a vehicles bicycles hikers and so this is an example of a standard cost function for a for a hybrid and maximum speed here is about at about the 3 degree downward slope I think and if you have a cost function for energy the most efficient I believe through receptive of the 10 degree downward slope so this is speed what we need is actually this long of the inverse of speed to In this equivalence and so the system is so consists of a 0 bps server other the graph historian cost grows with the post GIS extensions so we use the reading algorithms in this case the area start to waste our time and so it as an example of a nested BPS request we can use uh a shortest path at the coarse graph level as input to the shortest path a request at the next level etc. This is naturally implemented as a nested bps request of course you can do it otherwise so straightforward so this is client that my colleague work announced and implemented we currently have something like 30 to 40 of these once the services for various applications on ship traffic monitoring is 1 example from satellite image analysis and Bayesian analysis so this is the 1st
routine example what you smaller red flags marking the start and the end of the and and this is a this is an observer placed in the center of the image it's field of view and we want to avoid it so that affects the outcome of the reading of the all of the optimal route if you take away the observer this is the shortest path this is a topographic map it's about so it shows that the area is quite hilly on the next example is the example of hierarchical iteration in a very coarse grid the 1st solution to the 2nd and the final 5
so what are the next steps for us is to elaborate on the ground physics simulations which is really the region that office does the simulations such malls are used as as boundary conditions to weather forecast models but they can also be run independently offline as they say would perhaps more detailed background physics models and they're quite useful for predicting so that temperature profiles and which also implies that something about the load carrying capacity of the ground
of so the summary is that we we try to do path planning to rain it's still a work in progress I should say we have a situation dependent edge weights we use a hierarchical random graph service-oriented implementation WES and and post GIS system important complement for storing the graph with PG rooting uh shortest path algorithms on a so we we're on our
work heavily on the open source software so it's just to acknowledge that and the the final slide in
my my colleagues putting a joke here I don't know if anyone can spot but it's still a 0 lord will the we have a lot of use in Norway the the but been duped that few indeed questioned 1 of the and Hamilton environments of an enlightened but actually that's what we chose this area good lot to you wanted to test the algorithm in an area we know well so the and we also doing this portable orienteering so well known in the US but so we're all of whom had of that that's why you chose this this is and Northern side also all the time for the creation it any other questions think so so thank you very much
Resultante
Web Services
Prozess <Physik>
Automatische Handlungsplanung
Kartesische Koordinaten
Partikelsystem
Kombinatorische Gruppentheorie
Synchronisierung
Computeranimation
Standardabweichung
Knotenmenge
Punkt
Gewicht <Mathematik>
Graph
Datennetz
Punkt
Wort <Informatik>
Routing
Automatische Handlungsplanung
Computeranimation
Schätzwert
Task
Wellenpaket
Flächeninhalt
Graph
Schätzung
Automatische Handlungsplanung
Punkt
Kartesische Koordinaten
Routing
Überlagerung <Mathematik>
Automatische Handlungsplanung
Computeranimation
Bit
Gewicht <Mathematik>
Punkt
Physikalismus
Automatische Handlungsplanung
Implementierung
Kartesische Koordinaten
Information
Textur-Mapping
Computeranimation
Überlagerung <Mathematik>
Spezialrechner
Graph
Prognoseverfahren
Perspektive
Theoretische Physik
Diskrete Simulation
Datentyp
Implementierung
Serviceorientierte Architektur
Bildauflösung
Perspektive
Graph
Datenmodell
Profil <Aerodynamik>
Kanalkapazität
Automatische Handlungsplanung
Quick-Sort
Arithmetisches Mittel
Generator <Informatik>
Last
Information
Simulation
Beobachtungsstudie
Distributionstheorie
Ortsoperator
Graph
Kategorie <Mathematik>
Zufallsgraph
Voronoi-Diagramm
Polygonnetz
Regulärer Graph
Zellularer Automat
Zahlenbereich
Polygon
Roboter
Graph
Knotenmenge
Zufallszahlen
Regulärer Graph
Spieltheorie
Typentheorie
Datentyp
Wort <Informatik>
Diagonale <Geometrie>
Gerade
Einflussgröße
Wechselsprung
Datennetz
Typentheorie
Datentyp
Varianz
Normalvektor
Menge
Varianz
Computeranimation
Überlagerung <Mathematik>
Attributierte Grammatik
Gradient
Wald <Graphentheorie>
Sichtenkonzept
Momentenproblem
Datennetz
Kategorie <Mathematik>
Physikalismus
Polygon
Computeranimation
Weg <Topologie>
Polygon
Menge
Flächeninhalt
Digitale Photographie
Datennetz
Typentheorie
Datentyp
Zentrische Streckung
Graph
Dichte <Physik>
Selbstrepräsentation
Zufallsgraph
Relativitätstheorie
Zahlenbereich
Unrundheit
Knotenmenge
Auflösungsvermögen
Computeranimation
Übergang
Dichte <Physik>
Graph
Knotenmenge
Zufallsgraph
Mathematische Morphologie
Selbstrepräsentation
Attributierte Grammatik
Vorlesung/Konferenz
Triangulierung
Attributierte Grammatik
Konstante
Knotenmenge
Gewicht <Mathematik>
Gewicht <Mathematik>
Graph
Knotenmenge
Bildauflösung
Matrizenrechnung
Gewicht <Mathematik>
Graph
Matrizenrechnung
Element <Mathematik>
Schwach besetzte Matrix
Knotenmenge
Computeranimation
Schwach besetzte Matrix
Graph
Knotenmenge
Gewicht <Mathematik>
Flächentheorie
Selbstrepräsentation
Diagonale <Geometrie>
Satellitensystem
Distributionstheorie
Bit
Polygonzug
Selbstrepräsentation
Kartesische Koordinaten
Computerunterstütztes Verfahren
Rechenbuch
Analysis
Computeranimation
Übergang
Gradient
Gotcha <Informatik>
Kürzester-Weg-Problem
Client
Web Services
Algorithmus
Einheit <Mathematik>
Maßstab
Standardabweichung
Konditionszahl
Bilderkennung
Einflussgröße
Inklusion <Mathematik>
Zentrische Streckung
Lineares Funktional
Approximation
Kategorie <Mathematik>
Inverse
Quellcode
Ein-Ausgabe
Knotenmenge
Teilbarkeit
Hierarchische Struktur
Funktion <Mathematik>
Konditionszahl
Client
Kategorie <Mathematik>
Standardabweichung
Lesen <Datenverarbeitung>
Partitionsfunktion
Gewicht <Mathematik>
Physikalismus
Zahlenbereich
Feuchtigkeit
Unrundheit
Maßerweiterung
Physikalisches System
Open Source
Graph
Knotenmenge
Gewicht <Mathematik>
Theoretische Physik
Kostenfunktion
Schätzung
Kommunalität
Abstand
Cluster <Rechnernetz>
Maßerweiterung
Analysis
Leistung <Physik>
Soundverarbeitung
Algorithmus
Graph
Bildauflösung
Physikalisches System
Automatische Handlungsplanung
Inverser Limes
Abstand
Energiedichte
Bildschirmmaske
Minimalgrad
Flächeninhalt
Loop
Selbstrepräsentation
Kantenfärbung
Klumpenstichprobe
Greedy-Algorithmus
Physikalismus
Iteration
Computeranimation
Informationsmodellierung
Fahne <Mathematik>
Diskrete Simulation
Theoretische Physik
Luenberger-Beobachter
Vorlesung/Konferenz
Bildgebendes Verfahren
Normalvektor
Hierarchie <Mathematik>
Sichtenkonzept
Kanalkapazität
Profil <Aerodynamik>
Routing
Übergang
Office-Paket
Mapping <Computergraphik>
Benutzerprofil
Randwert
Datenfeld
Flächeninhalt
Last
Einheit <Mathematik>
Lesen <Datenverarbeitung>
Web Services
Hierarchie <Mathematik>
Offene Menge
Gewicht <Mathematik>
Graph
Open Source
Zufallsgraph
Implementierung
Physikalisches System
Automatische Handlungsplanung
Computeranimation
Graph
Hierarchische Struktur
Algorithmus
Zufallsgraph
Gewicht <Mathematik>
Arithmetische Folge
Software
Selbstrepräsentation
Implementierung
Serviceorientierte Architektur
Orientierung <Mathematik>
Algorithmus
Flächeninhalt
Güte der Anpassung
Vorlesung/Konferenz
Programmierumgebung
Computeranimation

Metadaten

Formale Metadaten

Titel The way to go with WPS
Serientitel FOSS4G Seoul 2015
Autor Messel, Espen
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben.
DOI 10.5446/32093
Herausgeber FOSS4G
Erscheinungsjahr 2015
Sprache Englisch
Produzent FOSS4G KOREA
Produktionsjahr 2015
Produktionsort Seoul, South Korea

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract How to find your way in difficult terrain, with obstacles, hazards, and deep snow? We present a solution for cross-country path planning and mobility, based on OSGeo software and open data. A large graph representing terrain, roads, and paths is stored in PostGIS for use with the pgRouting module of shortest path algorithms. The graph is based on detailed topography, soil type and vegetation data, and edge weights can be adapted for hikers and vehicles. The application is service oriented and held together by the Web Processing Service (WPS), the OGC interface standard for computation-oriented web services. A key component is the ZOO WPS server. The presentation will discuss WPS benefits and describe graph and weight generation, including challenges such as accounting for dynamic data about temporary hazards, weather, etc.

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