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In our recently published study “Direct local building inundation depth determination in 3-D point clouds from user-generated flood images” we present a new approach for deriving local building inundation depth from ordinary user-generated flood images captured during a flood event. After reconstructing a 3-D scene of the building of interest with close-range photogrammetry (CRP) algorithms, the flood line shown in the flood image is mapped into this 3-D scene and, thus, allows the derivation of inundation measurements. The acquired accuracy of building inundation depth with our automatic approach is 0.13 m ± 0.10 m. This result shows, that user-generated images are a valid, complementary data source for flood documentation.


Griesbaum, L., Marx, S. & Höfle, B. (2017): Direct local building inundation depth determination in 3-D point clouds generated from user-generated flood images. Natural Hazards and Earth System Sciences, 17 (7), pp. 1191-1201. DOI: 10.5194/nhess-17-1191-2017

A route generated from OpenRouteService showing landmarks for key decision points

A route generated from OpenRouteService showing landmarks for key decision points

A recently published paper (Rousell & Zipf 2017) presents a prototype navigation service extending OpenRouteService (Schmitz et al. 2008) that extracts landmarks suitable for pedestrian navigation instructions from the OSM dataset based on several metrics.

Where to select the inclusion of landmarks for pedestrian routes

Where to select the inclusion of landmarks for pedestrian routes

In general, when providing instructions on how to get somewhere, people generally include landmarks in their instructions - “Turn left just after the playground and then continue past the traffic lights. In widely used online routing services however, all instructions use the distance and direction format - “After 200m turn left, and then continue forwards for 400m”. As many people are not particularly accurate when judging distances travelled, this distance based method can often lead to missed turns or a general sense of unsurity. On the other hand, landmarks provide concrete features in the environment that are used to determine when to turn, and so the need for judging distance is reduced. Landmarks are different from just POIs, as they need to provide salient visual, structural or semantic cues the traveller can recognise easily. How to compute those from OSM has been explained in the paper.

The inclusion of landmarks in pedestrian routing instructions is now available online for all of Germany in LABS.openrouteservice.org for evaluation purposes.

The Landmark Service is currently available for all of Europe, but LABS supports only routing in Germany for testing purposes.

Enjoy testing!

Let us know what you think and where you want to see it or how to improve it.

Rousell A. and Zipf A. (2017): Towards a landmark based pedestrian navigation service using OSM data. International Journal of Geo-Information, ISPRS IJGI, 6(3): 64.

Acknowledgments
A part of the research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 612096 (CAP4Access). OpenRouteService supported by HeiGIT, core-funded by the Klaus Tschira Foundation, KTS, Heidelberg.

OpenRouteService:

Schmitz S, A Zipf, P Neis (2008): New applications based on collaborative geodata—the case of routing. Proceedings of XXVIII INCA International Congress on Collaborative Mapping and Space Technology. Gandhinagar, Gujarat, India.

Happy Birthday MapSwipe!

Already one year ago that MapSwipe was officially launched!

A big thank you to our contributors for your support - one year of tapping, swiping and putting families on the map!

In just one year thousands of users contributed more than 10 million taps and thereby provided crucial information on unmapped places.

In projects like shown below settlement areas (marked in green) were mapped in cooperation with the Missing Maps partners.

Classified buildings in Aweil (South Sudan)

This was just one of the many projects that were covered in the last couple of months, for the full overview please visit MapSwipe Analytics.

We are looking forward to many more years to come…

Some highlights of the first year include Integrating MapSwipe and HOT Tasking Manager , the 10th Million Contribution (introducing MapSwipe Analytics) and much more.

Yesterday, our colleague Dr. Martin Hämmerle defended his PhD thesis with great success. We would like to congratulate him - with our sincere thanks for his hard work and amazing research the last years.

Martin is an expert on 3D geodata (with focus on 3D point clouds from LiDAR and photogrammetry), methods for 3D processing and applications in environmental sciences and geosciences. Within his PhD he could publish numerous papers, which are well received by the research community and already frequently cited.

To take bathymetric measurements in areas with shallow water is very challenging for methods such as SONAR or bathymetric LiDAR. In our study “Assessing the Potential of a Low-Cost 3-D Sensor in Shallow-Water Bathymetry”, we examine the performance of a low-cost 3D camera when capturing bathymetry in waters up to 0.4 m depth.

The tested measurement setup provides 3D datasets with an accuracy and precision comparable to high-end approaches, and the maximum measurement range through water was found to be 0.3 m in outdoor conditions and 0.4 m in indoor conditions. Furthermore, a wave mitigation setup including a refraction correction procedure is introduced. Overall, the proposed measurement and processing setup offers a powerful complementary tool for research on 3D bathymetry in shallow waters.

The early access version of the paper is provided via
http://dx.doi.org/10.1109/LGRS.2017.2713991

Klopfer, F.; Hämmerle, M.; Höfle, B. (2017): Assessing the Potential of a Low-Cost 3-D Sensor in Shallow-Water Bathymetry. IEEE Geoscience and Remote Sensing Letters 14(8). http://www.dx.doi.org/10.1109/LGRS.2017.2713991

Noise pollution is a growing problem in many urban environments, affecting citizens’ daily life. It can reduce citizens’ happiness, increase their stress, and even people them get sick if they are exposed to noise pollution for a long period of time. In recent studies we investigate the use of crowdsourced data to derive noise polluted areas. Such information can also be used for generating routes that minimise exposure to noise.

Within urban environments, noise can be caused by many factors. Road traffic noise is one of the major noise sources. Besides, industrial areas, commercial places, and various buildings (such as chemical factories, power plants) also create noise. In the context of traffic noise emission, we mainly use OpenStreetMap data to derive the noise levels, according to the category of streets. A noise level is roughly approximated for each street segment based on the relevant attributes of roads (type of streets, traffic lights, number of lanes, speed limit, etc.) and surroundings. For example, a main street usually has more traffic than a residential street and thus has larger noise values and affects larger areas (buffers). This is inspired by earlier work by Martinelli and allows to estimate noise polluted areas and to generate a comprehensive coverage, currently only based on OpenStreetMap data. At the moment we investigate the quality of this modelled proxy data by comparison to official noise data in order to calibrate and improve those very first approximations.

With the initially derived noise information, a new experimental prototype feature ‘Quiet Routing’ is integrated into the LABS.OpenRouteService.org for evaluation purposes. This is adding to the “healthy” (stress reducing) routing theme, that has been introduced recently starting with the “prefer green areas” routing option.

The new ‘quite routing‘ feature in ORS can generate routes that avoid noisy areas. Moreover, an option of dynamically adjusting the weights is also added, allowing users to customize their routes and balancing between route distance and noise exposure. The same is now true for the “prefer green areas” option, that now also can be adapted individually by the user. For the sake of simplicity currently 10 levels are provided for each index. This leads to different weighting of the new attributes in comparison to the length of the route. So each user can decide individually how long a detour can be in comparison to the shortest route. Of course the results vary considerably with data quality and other factors what needs further ongoing investigations.

The feature also enhances the users’ awareness of noise along the route via the visualization of noise levels of route segments on the Web client. In addition to that these visualizations also help to understand the behaviour of the routing results according to the different weightings.

Currently the experimental prototype is available on the recently established LABS.openrouteservice.org platform together with other early features such as pedestrian routing through open spaces, the Places Location services API and others. These are availably initially only for all of Germany.

ORS Shortest Pedestrian Route without considering noise level

ORS Shortest Pedestrian Route without considering noise level

ORS QUITE Pedestrian Route considering noise

ORS QUITE Pedestrian Route considering noise

This work is inspired and supported through our cooperation project on urban stress (Psychogeography) with the Psychoepidemiologisches Zentrum (PEZ) at the Central Institute for Mental Health (ZI Mannheim) and the Intelligent Mobility Group at HeiGIT. The latter is kindly supported by the Klaus Tschira Foundation, Heidelberg through the core-funding for HeiGIT (Heidelberg Institute for Geoinformation Technology). Follow up research can for example analyse the effects of the individual choices and preferences, also further attributes are under consideration. We are looking forward to your feedback.

Open Position:

Software Engineer Geoinformation Technology / OpenStreetMap
Heidelberg Institute for Geoinformation Technology (HeiGIT)

In order to promote technology transfer and applied research in the area of ​​Geoinformatics, the Heidelberg Institute for Geoinformation Technology (HeiGIT) is currently being established with the support of the Klaus-Tschira Foundation. http://www.heigit.org. This is to be continued in the future as an independent institute. For this, we search a Software Engineer Geoinformation Technology. Depending on your experience the tasks are related to at least one of the following two areas:

A: Big Spatial Data Analytics (OSM, Social Web)

· Support designing and developing a queryable OSM Full History Geodatabase using Big Data Frameworks (Apache Ignite, Spark, Hadoop, etc.)

· Collaboration in the development of Web-based services for quality assessment and improvement of OpenStreetMap by data mining in OSM Full History data

· Development of methods and GI web services, especially for the analysis and data enrichment of heterogeneous geodata, especially from the social web, OpenStreetMap, remote sensing etc.

B: Route planning, Smart Mobility and Navigation Intelligence

· (Special) Routing with OSM, especially extensions of http://OpenRouteService.org based on Java

· Extension of the services infrastructure of various location-based services (LBS) using user-generated geodata, especially OSM.

We offer an attractive position in an interdisciplinary dynamic team in a highly dynamic growth market. HeiGIT is and will be related closely to the Department of Geoinformatics which is Member of the Interdisciplinary Center for Scientific Computing (IWR) and the Heidelberg Center for the Environment (HCE). We offer a stimulating interdisciplinary research environment with many personal development opportunities.

We expect an above-average university degree in one of the subjects of Geoinformatics, Computer Science, Geography, Mathematics or similar. Apart from a strong team spirit, independence and high motivation, as well as excellent abilities for internal and external communication and presentation in (if possible) German and English this includes excellent competency in methodology and technology, and geoinformatics experience, especially in Web Development, either in at least one the areas of ​​navigation, mobility & routing (Java) or in the area of ​​Big Spatial Data Analytics with Ignite & Spark (Java) or Spatial Data Mining & Machine Learning.

The position is to be filled as soon as possible and initially limited to June 2019 with the option of sustainable extension. Please send application documents (CV, certificates, references, etc.) as soon as possible, (best before mid August 2017) to zipf@uni-heidelberg.de

job description HeiGIT OSM big data, routing as pdf

we cordially invite everybody interested to our next open GIScience colloquium talk

“Heatmapping”: Accessing Geodatabases of Building Stocks for the Development of Spatial Energy System Models

Sebastian Blömer
ifeu Institut für Energie- und Umweltforschung Heidelberg

Time and date: Mon, July 10, 2.15 pm
Venue: INF 348, Room 015, Department of Geography, Heidelberg University

To evaluate the technological and economic feasibility of the use of different heat sources, conversion and supply structures, the development of high-resoluted GIS-Models of the long term energy demand for floor heating and hot water in residential and non-residential buildings became an important field of research in recent years. In this talk, I will give an overview of spatial energy system models of the German building stock from my work at the ifeu - Institute for Energy and Environmental Research Heidelberg. First, I will present the main requirements of spatial heat demand models of buildings with regard to the leading research questions and set them in relation with available geodatabases. As an insight into the development of nationwide heat atlases, I will present the procedure of a nationwide GIS-model of the development of the long term heat demand in the German residential building stock based on proprietary building footprint data. I will conclude with an overview of my recent work on integrating 3D-data and information on non-residential buildings into GIS-based heat demand models. As an Input for discussion, I will present the results of an evaluation of different machine learning algorithms for the automatic classification of settlement structures to enrich building footprint data.

Further dates and details: http://www.geog.uni-heidelberg.de/gis/veranstaltungen_en.html

Research in psychology and public health shows that there are environmental factors that cause an area to impose more or less stress to a person. One example is that being surrounded by natural green areas (meadows, parks, trees and forests etc. or also blue water areas) has a relaxing influence to the mood of a person in contrast to walking through a highly urbanised non-natural area which broadly speaking puts more stress on someone. Therefore a route that may not be the shortest path, but one that prefers the existing of green land-use areas to some extend can be seen as the more stress-free and healthy choice.

If you want to plan such a more pleasant or healthy route, you now can activate the “prefer green areas” option in the “additional settings” section of pedestrian routing options of OpenRouteService.
For testing purposes this specific feature which is targeted towards urban areas is currently restricted to Germany only (similar to other experimental prototypes at the brand new labs.openrouteservice.org).

This feature is still experimental, as the results vary strongly with the calculation of the weights for the green score. The latter depends both on the way of preprocessing and algorithms, as well as the data completeness of the green area data in OpenStreetMap. So expect some interesting results sometimes. Yet, the first route computations look often quite reasonable and promising. But of course this is a subjective measure, as different people will prefer different settings and accept detours of different lengths in favour of a greener or more healthy route. So there is much need for further research and development. We will be working on giving the user the option to interactively change the weighting in a more dynamic and fine-grained manner than it is possible at the moment in order to truly personalise her tour.

The calculated green index has also been added to the enhanced visualisation options for the route segments on the Web client. This allows the user for different categories (like surfaces, way types, steepness, suitability and now also for the green index ) to interactively overlay for each route segment the specific values on the current route using adapted colour codings.

Enjoy testing this new features on OpenRouteService.org

ORS Shortest Pedestrian Route (Mannheim)

ORS Shortest Pedestrian Route (Mannheim)

ORS GREEN Pedestrian Route (Mannheim)

ORS GREEN Pedestrian Route (Mannheim)

This work was inspired and supported through our cooperation project on urban stress (Psychogeography) with the Psychoepidemiologisches Zentrum (PEZ) at the Central Institute for Mental Health (ZI Mannheim) and the Intelligent Mobility Group at HeiGITThe latter is kindly supported by the Klaus Tschira Foundation, Heidelberg through the core-funding for HeiGIT (Heidelberg Institute for Geoinformation Technology). Follow up research can for example analyse the effects of the individual choices and preferences, also further attributes are under consideration.

Big thanks to the whole team for both preparing the landuse data (similar to OSMlanduse.org) based on OpenStreetMap and enhancing the routing service and client.

Recently Barrons studied the effect of Amazon.com buying Whole Foods for US$13.7 billion. They used the OpenRouteService Isochrones API for an detailed accessibility analysis of the whole US. With OpenStreetMap based OpenRouteService and US Census data they calculated how much US population is covered within different driving times from the US wide network of the 444 Whole Food stores plus the AmazonFresh/Pantry locations. They conclude that over 70% of the U.S. population, roughly 224 million people would be within one-hour delivery by Amazon and for example all of Manhattan within 10 minutes. Barrons sees this as a potential game-changer for Amazon, which tries to strengthen it’s “last-mile” delivery network as well as grocery sales.

The analysis demonstrates the power of accessibility analysis using OpenRouteService Isochrones API (the new version has been introduced here) for business analysis, site selection and logistics in nearly any domain based on OpenStreetMap data. Just imagine the power of combining this kind of analysis with the new OpenRouteService Places location service API. A early sneak preview of parts of that POI location search is available on the recently started Labs.OpenRouteService.org for experimental new features.

Read the whole Barrons study at:
Amazon and Whole Foods: How 2-Day Shipping Could Become an Hour or Less - With its deal for Whole Foods, Amazon will be within an hour drive of 70% of Americans.”
http://www.barrons.com/articles/amazon-and-whole-foods-how-2-day-shipping-could-become-an-hour-or-less-1498682000

https://openrouteservice.org/

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