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Current community efforts for Cyclone Idai in Mozambique and the Refugee Response in Venezuela again show the need and impact of humanitarian mapping.

Already before the start of the semester, on April 5th, disastermappers heidelberg will organize a Mapathon to support these efforts - this time in scope of the european Night of Geography!

The event was first introduced in 2017 by the French National Geographical Committee and then, due to the great success, scaled up to the whole of Europe in 2018. The main aim of the Night of Geography is

to put forward geography and geographers, give the greater public a chance to know them better, make geographical research more accessible.” (eugeo.eu)

In line with this and in collaboration with CartONG and PoliMappers Milan, and other organizations and communities all around Europe, we will therefore open our doors and get together to support the latests mapping projects.

When? Friday, April 5th, 6 pm

Where? Geographisches Institut, Berliner Straße 48

Everyone is welcome and no previous knowledge necessary as we will as usual provide an introduction to OpenStreetMap mapping.

We also reserved the PC Pools, however due to limited availability, please bring your own laptop and mouse if available.

Snacks and drinks will for sure be provided ;-)

We are looking forward to seeing you on Friday,

your disastermappers

See also the openrouteservice for Disaster Management: Response to Cyclone Idai by HeiGIT.

Topographic parameters of high-resolution digital elevation models (DEMs) with meter to sub-meter spatial resolution, such as slope, curvature, openness, and wetness index, show the spatial properties and surface characterizations of terrains. The multi-parameter relief map, including two-parameter (2P) or three-parameter (3P) information, can visualize the topographic slope and terrain concavities and convexities in the hue, saturation, and value (HSV) color system. Various combinations of the topographic parameters can be used in the relief map, for instance, using wetness index for upstream representation. In particular, 3P relief maps are integrated from three critical topographic parameters including wetness or aspect, slope, and openness data. This study offers an effective way to explore the combination of topographic parameters in visualizing terrain features using multi-parameter relief maps in badlands and in showing the effects of smoothing and parameter selection. The multi-parameter relief images of high-resolution DEMs clearly show micro-topographic features, e.g., popcorn-like morphology and rill.

blue arrow).

(a) Comparison of the 2P relief maps with slope–openness, (b) with slope–total curvature, (c) with slope–plan curvature, (d) hillshade map, and (e) ortho-image (popcorn-like morphology: green arrow and rill: blue arrow).

On Tuesday 26 March 2019, the 3DGeo Research Group of Prof. Bernhard Höfle and the newly founded Transdisciplinary Lab (TdLab) Geography (Dr. Nicole Aeschbach) hosted a workshop on “Emission Reduction in Smart Cities Using 3D Spatial Sensing and Analysis (ER3DS)” within the frame of the collaboration and exchange project ER3DS running from 2019 to 2020.

Participants of the first ER3DS workshop.

Participants of the first ER3DS workshop.

The aim was to integrate the knowledge of geography / geoinformatics, architecture, planning and environment, building energy use, and urban climate experts from Taiwan and Germany. Selected speakers from academia, SMEs and public authorities were brought together in order to assess the (future) role of 3D geodata and 3D analysis for emission reduction in the smart city context (e.g. smart building-integrated photovoltaics). As a result of the transdisciplinary workshop we exchanged know-how from multiple directions and we identified existing and new (joint) application examples. Moreover, we connected and linked new project partners in Heidelberg and Taiwan and we are able to address the main challenges across science and practice as well as between Germany and Taiwan.

Dr. Nicole Aeschbach and Prof. Bernhard Höfle opening the workshop.

Dr. Nicole Aeschbach and Prof. Bernhard Höfle opening the workshop.

Prof. Tzu-Ping Lin (National Cheng Kung University of Taiwan, Co-PI of the ER3DS project) talking about the application of urban 3D data for thermal, wind, and energy issues in urban areas.

Prof. Tzu-Ping Lin (National Cheng Kung University of Taiwan, Co-PI of the ER3DS project) talking about the application of urban 3D data for thermal, wind, and energy issues in urban areas.

Prof. Chi-Kuei Wang (National Cheng Kung University of Taiwan, PI of the ER3DS project) talking about the application of low resolution data for emission reduction in the smart city context.

Prof. Chi-Kuei Wang (National Cheng Kung University of Taiwan, PI of the ER3DS project) talking about the application of low resolution data for emission reduction in the smart city context.

Prof. Bernhard Höfle giving a presentation how to benefit from 3D geographic point clouds to reduce emissions.

Prof. Bernhard Höfle (PI of the ER3DS project) giving a presentation how to benefit from 3D geographic point clouds to reduce emissions.

Prof. Andreas Matzarakis (University of Freiburg and German Meteorological Service) giving a talk about short term and long term actions in cities in the era of climate change.

Prof. Andreas Matzarakis (University of Freiburg and German Meteorological Service) giving a talk about short term and long term actions in cities in the era of climate change.

Discussions during a poster session.

Discussions during the "expert marketplace".

Raino Winkler (City of Heidelberg, Environment Department) presenting a poster on climate change adaption strategies in the city of Heidelberg.

Dr. Raino Winkler (City of Heidelberg, Environment Department) presenting a poster on climate change adaption strategies in the city of Heidelberg.

Many thanks to the IWR-Mathematikon administration for supporting the workshop with a perfect venue, and also to the helping hands in the background (Bettina Knorr, Carina Altmeyer and Hannah Weiser).

Find more information about the ER3DS project on the project website and in the GIScience News Blog. In case you want to contribute with your experience and knowledge about ER3DS, feel free to contact us directly.

The project ER3DS is funded by BMBF (Germany, FKZ 01DO19001) and MOST (Taiwan).

Welcome back to a new episode of how to become ohsome. Yes, you’ve read the heading correctly. We are really talking about a snake in a notebook on another planet. If you are familiar with one of the most used programming languages in the GIS world, you might already know by now which snake is meant here. We will show you in a Jupyter Notebook how you can use Python to make ohsome queries and visualizations in one go. And we will do that through using our global ohsome API instance. In case you’ve just read the combination of “global” and “ohsome” for the first time, better get up-to-date and read this blog post.

As already mentioned, Python is a widely used programming language, especially in the GIS world, to perform spatial analysis and create visualizations like diagrams. Combining Python code, explanations and visualizations in one go, a Jupyter Notebook is a useful tool to achieve just that. It is already in use within other projects in HeiGIT (e.g. avoid obstacles with ORS). So we thought it was time to make Jupyter Notebooks ohsome.

To give you a little teaser of what is in that notebook, the following shows a visualization plus a piece of Python code that is used to create it. The diagram displays the count of OSM elements having the OSM tag building for different points in time for the three cities Heidelberg, Mannheim and Ludwigshafen.

And here is a part of the Python code that is used in the notebook to create the visualization above:

data = [trace1, trace2, trace3]
layout = go.Layout(
   title = 'Number of OSM buildings in Heidelberg, Mannheim and Ludwigshafen',
   barmode = 'group',
   legend = dict(orientation = "h")
)
fig = go.Figure(data = data, layout = layout)
py.iplot(fig, filename = 'groupBy')

The complete Jupyter Notebook with all the code and explanations can be found here. As always, if you want to give us feedback or have any questions, info@heigit.org is the best way to get in touch with us. Further Jupyter Notebooks with more examples will follow soon. Stay ohsome!

The Humanitarian OpenStreetMap Team (HOT) has launched an activation to support humanitarian operations responding to the impact of Cyclone Idai. These efforts were already supported by more than 1500 mappers of the global OpenStreetMap (OSM) community that contribute geodata about the affected regions in Mozambique and the surrounding countries.

The team at HeiGIT (Heidelberg Institute for Geoinformation Technology) for sure is also supporting these efforts through their services and applications that facilitate efficient use of the OSM data provided. The openrouteservice for disaster management now provides updates almost hourly (every 1.5 h) to consider always the latest OSM data available.

To better support the response to Cyclone Idai we have furthermore integrated latest flood extents provided by the Compernius Emergency Management Service, Activation EMS348. To avoid those areas, calculate your route, draw a polygon around the areas you want to avoid, and the route will automatically be recalculated.

You can use it here: https://disaster.openrouteservice.org/

We are currently evaluating further flood extent layers to be integrated in our routing service. We will keep you updated.

The flooding data is also available as Web Map Service (WMS) layer. Also related to disasters is a map layer called “OSM Elements at Risk” showing critical infrastructures and similar objects that need special attention in disasters based on OpenStreetMap data. Please contact us if you need access to one of those map layers.

We are always happy to support citizen science projects at the HeiGIT. HeiGIT/ GIScience efforts already range from tools that assess the data quality of citizen science projects (see, e.g., this blog post about “Plausible Parrots“) to approaches related to data creation, like MapSwipe Analytics (learn more here).
Currently, we are supporting citizen science approaches towards new applications in Arctic and climate research. Mapping the Arctic ground ice distribution has been identified as a major scientific task to bring climate change impact assessments and permaforst modeling to a new level of realism. Dr. Moritz Langer, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research (AWI), was presenting first ideas to tackle this challenge by integrating citizen science at the ThinkCamp CitizienScience@Helmholtz.
HeiGIT and AWI are further collaborating in an exhibition that was developed for the MS Wissenschaft science ship: Artifical Intelligence. Our aim is to combine the potential of both, citizen science and machine learning approaches, to deepen our understanding of earth surface processes.

Das HeiGIT unterstützt das Bundesamt für Kartographie und Geodäsie (BKG) dabei eine Frage aus der “Die Maus”-Redaktion zu beantworten: Welcher ist Deutschlands nördlichster & südlichster Ort, und wie groß ist die fußläufige Distanz dazwischen?

Der Beitrag war am 23.03.2019 in der “Frag doch mal die Maus Show” zu sehen.

Wollen Sie wissen, wie weit es vom westlichsten zum östlichsten Ort in Deutschland ist? Dann schauen Sie doch mal beim openrouteservice vorbei.

At Saturday the 23rd of March 2019, it was time again for very young researchers being introduced to GIScience. Melanie Eckle, Martin Hilljegerdes, Sven Lautenbach, Katharina Przybill, Leonie Schuchardt and Vivien Zahs introduced 16 highly motivated kids into GIScience as part of the KinderUni 2019. After an overview and an introduction into desktop mapping and how to help in humanitarian mapping with MapSwipe, we went out for a scavenger hunt. The kids navigated the Neuenheimer Feld based on absolute and relative coordinates using maps and GPS receivers to locate the treasure – but this year’s highlight was the “group picture” taken by a laser scanner in front of the famous Hettner rock, which allowed to measure the kids’ height with high precision. A screenshot of the point cloud together with a traditional group picture with the rabbits before the physics building and a self created uMap with all relevant events along the route will remind young participants of their first GIScience contact – maybe in the not so far future we will see some of them again as students, scientist and professors at GIScience.

Point cloud of upcoming young GIScientists together with height measurement.

Point cloud of upcoming young GIScientists together with height measurement.

The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible, helping to move from a two-dimensional (2D) or three-dimensional (3D) data representation of four-dimensional (4D) data structures, where the time variable adds new information - and challenges - for information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded according to the different paths taken by each research community. This article brings together the advances of multisource and multitemporal data fusion approaches with respect to the various research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references. More specifically, this work provides a bird’s-eye view of many important contributions specifically dedicated to the topics of pansharpening and resolution enhancement, point cloud data fusion, hyperspectral and lidar data fusion, multitemporal data fusion, and big data and social media. In addition, the main challenges and possible future research in each area are outlined and discussed.

Max is two and loves spending Monday afternoons with his Dad at the playground. Finding a suitable playground however isn’t easy, since a few criteria must be met: there should be a bench and some trees nearby to get shelter from the sun and an ice cream shop within the neighbourhood. Using the new spatial join functionality of the HeiGIT OpenStreetMap History Database OSHDB answering this question can easily be done with just one query. The new methods will make it possible to query OSM objects based on their spatial relation and proximity to other OSM objects. So using the OSHDB Max’ playground query might look like this:

OSHDBMapReducer
.osmTag(“leisure”, “playground”)
.neighbouring(25., “amenity”, “bench”)
.neighbouring(25., “natural”, “tree”)
.neighbouring(300., “cousine”, “ice_cream”)
.collect()

This query reduces the number of potential playgrounds within the city of Heidelberg from 171 to 4 as shown in the map below.Similar questions like this are also addressed within the mFund meinGrün project, which aims at extracting information about urban green spaces and the facilities and activities they provide using diverse data sources such as OSM, Social Media and satellite imagery helping citizens to find the most suitable green space for their needs, e.g. through green and healthy routing in Openrouteservice.

But this is by far not the only case where OSHDB spatial queries are useful. They are also a valuable tool when it comes to measuring data quality of OpenStreetMap. The completeness of building addresses for instance is an important quality measure for the applicability of routing services in a certain area. Building addresses in OSM are tagged with the key “addr:housenumber” which can be set in two different ways: as an additional tag of a “building” feature or as a node located within a “building” feature. For the latter case, the number of buildings containing the tag “addr:housenumber” as a node can be queried using the new method contains().

OSHDBMapReducer
.osmTag(“building”)
.contains(“addr:housenumber”)
.count()

By extending this OSHDB query by a few more lines of code we can take into account both tagging schemes to derive the total percentage of buildings containing information about the house number. Applying this query to the city of Heidelberg yields the map below. Further quality measures like this are being developed within the DFG Project A framework for measuring the fitness for purpose of OpenStreetMap data based on intrinsic quality indicators and HeiGIT.

The spatial join functionality of the OSHDB is still at the development stage. If you want to stay up to date, check out our OSHDB GitHub repository. Stay tuned for further ohsome news!

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