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Dear Mapping Enthusiasts,

in the upcoming week we want to invite you to our last Mapathon of the year 2016!

When: 08.12.2016, 18:00

Where: Hörsaal, Berliner Straße 48

In this mapping event we will map buildings and other infrastructures in Karakol, the fourth largest city in Kyrgyzstan, which is located in a seismically active area of Tian Shan.

To develop an earthquake monitoring network in this area, the German Research Centre for Geoscience (GFZ) is involved in the construction of earthquake early warning systems. These are considered to be an effective, pragmatic and viable tool for seismic risk reduction in cities. The locations of the population and infrastructures are of great importance for the site selection to achieve efficiency in on-site earthquake warning and rapid response.

More information about this project will be provided at the beginning of the mapping event in an online Skype talk by Massimiliano Pittore of the GFZ Potsdam.

A detailed introduction into mapping in OpenStreetMap will be provided afterwards to enable everyone to take part and learn more about crisis mapping- therefore no previous knowledge needed, just bring your laptop and mouse if available!

To get you into the right christmas mood, we will also provide Glühwein, christmas snacks and soft drinks!

We are looking forward to seeing you on Thursday!

Recently a book chapter on some of our work in the MayaArch3D project on “Structuring Archaeological Data to Deliver Interactive, Transparent 3D Reconstructions in a 3D WebGIS” has been published in the edited book “3D Research Challenges in Cultural Heritage II”.

Creating 3D reconstructions is a common approach today in archaeology and cultural heritage. The problem is that 3D models in online virtual research environments may tempt users to believe them as historical truth. What must be done to enable the public to view a 3D reconstruction as a hypothesis and have access to the supporting data? This paper explains – via use-case examples from the ancient Maya city of Copan, Honduras – a procedure for structuring heterogeneous data to enable interactive, web-based access to 3D reconstructions of cultural heritage. A prototype 3D WebGIS system was built that can store, manage, and visualize 3D models and integrates these with georeferenced archaeological data. An ontology was created, a segmentation pipeline was developed, and databases and services were designed to structure and integrate the data in the 3D WebGIS. Results include two interactive 3D reconstructions: a city model and a temple model – these demonstrate how proper data structuring can deliver transparent models for archaeological argumentation.
The 3D WebGIS developed by the MayaArch3D Project is a prototype solution for web-based visualization and information systems to link 3D objects to other forms of information and make them traceable and accessible and available for further analysis on a multimedia level. The pipeline for segmenting and structuring 3D data has already been published (Auer et al. 2014); we have followed this here and have explained the process and challenges of preparing our data for this pipeline. Two queryable models are presented in the system: a low-resolution city model of Copan, and a high resolution temple reconstruction. These demonstrate the system’s potential for offering interactive access to knowledge about 3D reconstructions.

Schwerin, J., Lyons, M., Loos, L., Billen, N., Auer, M. Zipf, A. (2016):
Show Me the Data!: Structuring Archaeological Data to Deliver Interactive, Transparent 3D Reconstructions in a 3D WebGIS. In: Münster, S., Pfarr-Harfst, M., Kuroczyński, P., Ioannides, M. (Eds.): 3D Research Challenges in Cultural Heritage II -How to Manage Data and Knowledge Related to Interpretative Digital 3D Reconstructions of Cultural Heritage. Springer. pp. 198-230.

Auer, M., Agugiaro, G., Billen, N., Loos, L., Zipf, A. (2014): Web-based visualization and query of semantically segmented multiresolution 3D models in the field of cultural heritage. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-5, pp. 33–39 (2014). doi:10.5194/isprsannals-II-5-33-2014,

Liebe Studenten und Mitarbeiter der Universität Heidelberg, wir brauchen eure Hilfe.

Das CampusMobil-Team der Universität Heidelberg entwickelt derzeit eine Navigations-App, damit sich endlich jeder in den Weiten des Universitäts-Campus zurechtfinden kann. Damit man aber nicht einfach vor der Wand des Ziel-Gebäudes, sondern am Eingang des jeweiligen landet, wollen wir zunächst die verschiedenen Eingänge der Universitätsgebäude digitalisieren.
Das heißt kurzum, dass wir gemeinschaftlich Daten aus analogen Gebäudeplänen in die OpenStreetMap Datenbank übertragen.

Wir suchen interessierte Leute, die an ein oder zwei Abenden im Dezember ein paar Stunden (etwa 3h) Lust und Laune haben mitzumachen. Hierzu benötigt ihr keinerlei Vorkenntnisse, denn wir erklären euch genau was gemacht werden muss. Perfekt wäre es, wenn ihr eure Laptops mitbringen könntet.
Für eine gemütliche Atmosphäre sorgen wir mit kostenlosen Getränken und weihnachtlicher Musik!

Die Events finden am 6. Dezember 2016 ab 18.30 Uhr und am
15. Dezember ab 18.00 Uhr in der Berliner Straße 48, Universität Heidelberg, Geographisches Institut (Haltestelle Technologiepark) im großen Hörsaal (EG) statt.

Sollten wir euer Interesse geweckt haben, meldet euch bitte alsbald und möglichst bis zum 30. November bei:
Amandus Butzer butzer@stud.uni-heidelberg.de

Falls ihr nur an einem Termin Zeit habt, oder erst später dazukommen könnt, ist das kein Problem, Hauptsache ihr seid dabei !

This week’s “OSM Wiki “Image of the Week”” shows two maps from our efforts to use MapSwipe data for defining and prioritising OSM Hot Tasking Manager tasks.
We appreciate the recognition :-)
The workflow and respective tools have been introduced recently and are based on the “Heidelberg Process“, that lead to the development of MapSwipe though MissingMaps.org after Benjamin’s initial Web-based implementations using Pybossa.


MapSwipe news

This week Alexander Zipf was giving an invited keynote presentation at the 18th Geoinfo Conference in Campos do Jordão, São Paulo, Brazil.

The GEOINFO conferences aim to bring together leading GIScience and spatial database researchers, to present to the local community a perspective of the state-of-the-art in the area. Past speakers have included Max Egenhofer, Gary Hunter, Andrew Frank, Roger Bivand, Mike Worboys, Werner Kuhn, Stefano Spaccapietra, Ralf Guting, Shashi Shekhar, Christopher Jones, Martin Kulldorff, Andrea Rodriguez, Max Craglia, Stephen Winter, Edzer Pebesma, Fosca Giannotti, Christian Freksa, Thomas Bittner, Markus Schneider, Helen Couclelis, Randolph W. Franklin, Paul Brown and Michael Batty.

The title of the talk by Alexander Zipf was:
Analysing and Improving Volunteered Geographic Information for Humanitarian Activities

Up-to-date and comprehensive geographical information is essential for the planning and implementation of humanitarian aid in the context of crises and disasters. In addition to the established data sources of professional organisations - such as remote sensing, government and commercial data - alternative options have gained importance in recent years. These new options include different types of information that are contributed by volunteers e.g. geographical information based on disaster mapping activities on platforms such as OpenStreetMap, but also spatial information extracted from various social media platforms like Twitter or Flickr. The automated evaluation of these data from social media currently presents an interesting challenge. Other important questions are: How can we analyse, evaluate and improve the quality of these new data? And how can we use them effectively in the context of disaster management? The presentation will discuss a number of methods that we have devised to help us find answers, in particular with respect to OSM quality assessment and improvement, the development of new microtasking apps such as MapSwipe and also the spatiotemporal analysis of social media data.

In agriculture, information about the spatial distribution of crop height is valuable for applications such as biomass and yield estimation, or increasing field work efficiency in terms of fertilizing, applying pesticides, irrigation, etc. Established methods for capturing crop height often comprise restrictions in terms of cost and time efficiency, flexibility, and temporal and spatial resolution of measurements. Furthermore, crop height is mostly derived from a measurement of the bare terrain prior to plant growth and measurements of the crop surface when plants are growing, resulting in the need of multiple field campaigns.

In our study, we examine a method to derive crop heights directly from data of a plot of full grown maize plants captured in a single field campaign with a low-cost 3D camera. The results of the study are now published as:

Hämmerle, M. & Höfle, B. (2016): Direct derivation of maize plant and crop height from low-cost time-of-flight camera measurements. Plant Methods 2016(12:50). doi:10.1186/s13007-016-0150-6

Side view on the maize field point clouds captured with a low-cost 3D camera.

Side view on the maize field point clouds captured with a low-cost 3D camera.

We examine single measurements captured with the 3D camera and a combination of the single measurements, i.e. a combination of multiple perspectives. The quality of both CHMs, and individual plant heights is improved by combining the measurements. The crop heights derived from the 3D camera measurements comprise an average underestimation of 0.06 m compared to terrestrial laser scanning (TLS) reference values.

Schematic drawing of frontal view on field experiment mountings. (a) 3D camera mounting with camera in nadir perspective over maize field, (b) TLS mounting with tilted scanner to account for nadir field of view restriction.

Schematic drawing of frontal view on field experiment mountings. (a) 3D camera mounting with camera in nadir perspective over maize field, (b) TLS mounting with tilted scanner to account for nadir field of view restriction.

We recommend the combination of multiple low-cost 3D camera measurements, removal of measurement artefacts, and the inclusion of correction functions to improve the quality of crop height measurements. Operating low-cost 3D cameras under field conditions on agricultural machines or on autonomous platforms can offer time and cost efficient tools for capturing the spatial distribution of crop heights directly in the field and subsequently to advance agricultural efficiency and productivity. More general, all processes which include the 3D geometry of natural objects can profit from low-cost methods producing 3D geodata.

Detailed view on the point clouds of upper part of two maize plants. (a) point cloud captired with low-cost 3D camera. (b) TLS point cloud

Detailed view on the point clouds of upper part of two maize plants. (a) point cloud captired with low-cost 3D camera. (b) TLS point cloud

Check-in data such as provided by Foursquare is one kind of social media feeds which gained considerable interest over recent years. This interest is partly due to their high degree of semantic detail, given that users check-in at places which are categorized by a relatively well-defined taxonomy. One associated prevalent task, in research as well as practical applications, is predicting human behaviour from such datasets. Assessing the success of predictions is, however, a complicated task given that the theoretical predictability inherent to such datasets remained largely unknown so far. In a recent study, which was published in Geoinformatica, we investigated the bounds of this inherent predictability of Foursquare datasets, with respect to their power in forecasting future spatial and temporal check-ins. We found that, for three exemplary yet representative datasets from Chicago, New York City and Los Angeles, the predictability ranges on an interval of [27%, 92%]. This result indicates that a certain level of accuracy will be reached even by the worst prediction algorithms. Further, this result allows estimating the relative performance of prediction algorithms. We also investigated the influence of check-in frequencies on the predictability. Our results show that the individual user-based check-in frequency has no or little effect. That is, the majority of users tend to be relatively regular with respect to their online check-in behaviour. In contrast, the check-in frequencies associated with places and time slots are negatively correlated with predictability. In other words: the cumulative mixture of people contributing to places and time slots leads to an increased level of semantic complexity, in turn, lowering predictability. However, the latter outcome also indicates great leverage effects and therefore offers potential for improving prediction algorithms.

Li, M., Westerholt, R., Fan, H. et al. (2016): Assessing spatiotemporal predictability of LBSN: a case study of three Foursquare datasets. Geoinformatica (2016). Online First Nov 2016. doi:10.1007/s10707-016-0279-5.

You can also check out a freely accessible version here: http://rdcu.be/mZud

Today a new global WebMap prototype “OSMLanduse.org” has been launched by GIScience Research Group Heidelberg. The map provides worldwide Landuse/Landcover information on the basis of OpenStreetMap (OSM) data. This is based on our earlier work on testing the suitability of OpenStreetMap for deriving landuse and landcover information (LULC).
LULC data is highly relevant for many research questions and practical planning activites.
Up to now there exist well known data sets generated from remote sensing imagery such as CORINE, Urban Atlas or GlobeLand30, which are available for different areas, time slots, and offer different LULC classifications.
Yet it is an interesting question if and to what degree OpenStreetMap can complement, add to, or refine these sources. Up to now this is certainly very different in different world regions and the new map helps to explore and better understand this kind of data in OSM and how it is evolving in different areas.
Therefore our aim is to evaluate the overall possibility and suitability of OpenStreetMap (OSM) data for these specific purposes, identify ways for improvement and to provide all this information globally to the interested communities in an automated way.

In order to do so, the data from OpenStreetMap, stored in Key-Value pairs, was initially categorized similar to the classification level 2 of the CORINE Landcover classes. This category mapping and further pre-processing of OSM data will be further refined in ongoing work.
We then set up a first basic WebMapping application with the use of free and open-source software, including PostgreSQL/PostGIS, Geoserver and MapProxy.
At the moment the website provides basic WebGIS elements, such as legend, search function and feature info. Higher zoomlevels (>7) are updated on a minutely basis. In the near future we want to implement other useful features and explore ways to measure the quality of the data.
Right now we are working on realizing a function, which provides statistical information about the landuse/landcover in a certain area (bbox), defined by the user.
In particular we also do further refine the mapping between common land use classifications such as CORINE, Urban Atlas etc. and the OpenStreetMap categorization of objects and improve the preprocessing for different scales etc.
So stay tuned for further updates!

http://osmlanduse.geog.uni-heidelberg.de/ == http://osmlanduse.org/

This work has kindly been supported by the Klaus Tschira Foundation (KTS) Heidelberg in the context of establishing the Heidelberg Institute for Geoinformation Technology (HeiGIT).

Earlier Work:

Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., (2015): Quality assessment of the contributed land use information from OpenStreetMap versus authoritative datasets. In: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M., OpenStreetMap in GIScience: experiences, research, applications. ISBN:978-3-319-14279-1, PP. 37-58, Springer Press.

Jokar Arsanjani, J., Helbich, M., Bakillah, M., Hagenauer, J., & Zipf, A. (2013). Toward mapping land-use patterns from volunteered geographic information. International Journal of Geographical Information Science, 2264-2278. DOI:10.1080/13658816.2013.800871.

Dorn, H., Törnros, T. & Zipf, A. (2015): Quality Evaluation of VGI using Authoritative Data – A Comparison with Land Use Data in Southern Germany. ISPRS International Journal of Geo-Information. Vol 4(3), pp. 1657-1671, doi: 10.3390/ijgi4031657

Hagenauer, J. & Helbich, M. (2012): Mining urban land use patterns from volunteered geographic information using genetic algorithms and artificial neural networks. International Journal of Geographical Information Science (IJGIS). Taylor & Francis. DOI:10.1080/13658816.2011.619501.

Ballatore, A. and Zipf, A. (2015): A Conceptual Quality Framework for Volunteered Geographic Information. COSIT - CONFERENCE ON SPATIAL INFORMATION THEORY XII. October 12-16, 2015. Santa Fe, New Mexico, USA. Lecture Notes in Computer Science, pp. 1-20.

Törnros, T., Dorn, H., Hahmann, S., and Zipf, A. (2015): Uncertainties of completeness measures in OpenStreetMap - A Case Study for buildings in a medium-sized German city, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W5, 353-357, doi:10.5194/isprsannals-II-3-W5-353-2015.

Fan H., Zipf A., Fu Q. and Neis P. 2014. Quality assessment for building footprints data on OpenStreetMap. In: International Journal of Geographical Information Science. DOI: 10.1080/13658816.2013.867495

Our recent team member Dr. Yingwei YAN successfully defended his PhD thesis this very week.
We do congratulate him most cordially!
The thesis was conducted at the National University of Singapore at the Department of Geography before he joined the GIScience Heidelberg team. Yingwei worked for example on using fuzzy set theory to assure the quality of VGI and used methods such as neural networks to predict crop pest risks associated with global climate change.
For his work he was also granted the Esri Young Scholar Award 2016.
We are looking forward to work with him in the area of VGI quality analysis in disaster management and related modeling approaches.

On November 24th, “Fachaustausch Geoinformation“, a networking  event  for professionals in the geodata and geoinformation domain, took place in Heidelberg. It is organised every year by GeoNet.MRN, bringing together people, companies and institutions from the Rhein-Neckar region and beyond who are active in GIS, geoinformatics and GIScience. As in previous yeas, the GIScience research group at Heidelberg University contributed to the event in various ways. We presented current projects and new research in the foyer of the Print Media Academy, sparking conversations and discussions with visitors from the industry, and from municipalities and research institutions. Jun.-Prof. Bernhard Höfle hosted one of the event’s sessions, with talks about current developments in 3D data acquisition and geodata management. Melanie Eckle gave a talk on the use of user-generated data (especially OpenStreetmap data) in flood risk management and emergency planning with OpenFloodRiskMap. Numerous members of our research group took part in the event, using the chance to talk to people with different backgrounds and perspectives.

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