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

The speaker is Prof. Jochen Albrecht
Professor of Computational and Theoretical Geography, Hunter College, City University of New York

When: Monday 16.07.2018, 2:15 pm

Where: INF 348, room 015 (Institute of Geography, Heidelberg University)

Open Source Foundations for Spatial Decisions Support Systems

Spatial Decision Support Systems (SDSS) were a hot topic in the 1990s, when researchers tried to embue GIS with additional decision support features. Successful practical developments such as HAZUS or CommunityViz have since been built based on commercial desktop software and without much heed for theory other than what underlies their process models. Others, like UrbanSim, have been completely overhauled twice but without much external scrutiny. Both, the practical and theoretical foundations of decision support systems have developed considerably over the past 20 years and I will present an overview of these developments and then take a look at what corresponding tools have been developed by the open source communities. In stark contrast to the abundance of OpenGeo software, there is currently no open source SDSS. The presentation will therefore conclude with a discussion of different approaches that lend themselves to be used as platforms for us to develop an open source framework to build an SDSS according to our needs.

Members of the HeiGIT team were presenting parts of our work at this years AGIT/GI_Forum conference in Salzburg, Austria (as already announced in a previous blogpost).

Julian Bruns was presenting the results of a joint work with the KIT and his old employer, the FZI, which is published in the GI-Forum journal (English conference running in parallel to the AGIT), as well as the rich diversity of our routing software openrouteservice.

Fabian Kowatsch was giving a talk about the ohsome platform explaining the current status-quo of the development, which was published as a short-paper in the German language AGIT-journal.

Additionally to the talks, a poster of the GIScience group and a poster of the HeiGIT were presented in the poster session:

HeiGIT

Global Climate Protection Map

The discussions and the interest at the conference showed that our developments are useful for and acknowledged by the GI-community, researchers and users alike.

The study “Mobile low-cost 3D camera maize crop height measurements under field conditions” (see the respective blog entry) is now available also as print version:

The updated citation is:

Hämmerle, M. & Höfle, B. (2018): Mobile low-cost 3D camera maize crop height measurements under field conditions. Precision Agriculture 19(4), pp. 630-647. doi: 10.1007/s11119-017-9544-3.

For the fourth consecutive year, the 3DGeo team acquired the rock glacier Äußeres Hochebenkar in the Austrian Ötztal by terrestrial laser scanning. This adds another point cloud to the multitemporal dataset to observe how the active rock glacier changes over the years.

In an elevation over 2600 m a.s.l., the complex terrain on and around the rock glacier greatly challenges the ground-based survey to obtain these high-resolution data of unmatched precision. However, the insights to be gained from the continuous observation - besides the experience of the beautiful Alpine landscape - makes up for every effort.

You would like to experience this impressive high-mountain environment yourself and are interested in innovative geodata processing methods in mountain research? Then the Innsbruck Summer School of Alpine Research 2019 is the perfect opportunity! In related blogposts, you find impressions of the first and second editions of the now established summer school.

Ever wondered how you can generate/use some meta-information about OpenStreetMap for your project? Are you interested in visualizing different aspects of OpenStreetMap data?

In the article below, we present a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results’ reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too.

Have fun reading it!

Mocnik, F. B., Mobasheri, A., & Zipf, A. (2018). Open source data mining infrastructure for exploring and analysing OpenStreetMap. Open Geospatial Data, Software and Standards, 3(1), 7.

Technical overview of the proposed infrastructure

Technical overview of the proposed infrastructure

Calendar heat map with temporally arranged cells

Calendar heat map with temporally arranged cells

Visualization technique for the documentation of the folksonomy in the OSM wiki

Visualization technique for the documentation of the folksonomy in the OSM wiki

Further information about the analysis of the evolution of the OSM Wiki and tag folksonomy is given here:

Mocnik, F.-B., Zipf, A., Raifer, M. (2017): The OpenStreetMap folksonomy and its evolution. Geo-spatial Information Science. Taylor & Francis. DOI: 10.1080/10095020.2017.1368193.

Because of several requests the deadline for short papers has been extended to Wed. July 25 2018.

The recent availability of user-generated geographic datasets allows gaining novel insights into otherwise hardly observable societal phenomena. Geosocial media forms one important source of user-generated information, which partly describes the everyday lives of people. The analysis of these kinds of data, however, requires new approaches. Geosocial media data— like those extracted from Twitter, Flickr, Instagram, and others — differ from established sources in that they are largely inherently platial in nature. People provide their own subjective opinions or perceptions, and taken together these represent the digital social imagination of places. Crisp and objective geographic data primitives like points, lines or polygons are not necessarily the preferable units for analysing these kinds of information. Platial analysis approaches are thus needed to fully exploit the potential of geosocial media and related data. Yet, while human geographers and social scientists have been theorizing on the concept of place since a long time, and despite of invocations by leading GIScience researchers, we are still lacking a universal theory on the formalization of places and how to make them available to quantitative and other GIS-related analysis strategies. Partly, this lack has been due to the insufficient availability of platial data, but the appearance of geosocial media might change this condition. It is therefore time to rethink our geographical analysis strategies with a focus on “place” instead of “space”.

We accept short paper submissions of 3,000 words / 7 pages maximum length. All accepted short papers will be published with CEUR-WS, a community-driven publication outlet for workshop and conference proceedings from computer science and information systems. We further invite authors to extend their short paper contributions to long papers, which could then be submitted to an adjoint special issue to be published in Transactions in GIS after the workshop.

For more information check out http://platial18.platialscience.net. Stay tuned on Twitter: #platial18 @GIScieceHD

Important Dates

1 June 2018: Call for short papers opens
1 June 2018: Registration opens
25 July 2018: Extended Submission deadline for short papers
19 August 2018: Camera-ready papers are due
16 September 2018: Papers are available online
21 September 2018: VGIscience PLATIAL’18 workshop

http://platial18.platialscience.net

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

The speaker is Prof. Jiangya Gong
Dean, School of Remote Sensing and Information Engineering, Wuhan University, China

When: Monday 09.07.2018, 2:15 pm

Where: INF 348, room 015 (Institute of Geography, Heidelberg University)

Big spatio-temporal data analysis based on social sensing

With the rapid development of information and communications technology (ICT), ubiquitous social sensing data bring new opportunities for us to understand our socio-economic environments. We use the term social sensing to represent the study of characteristics of human spatio-temporal behavior, and the discovery of socio-economic environments, by various means of social sensing. This talk introduces the concept of social sensing and social sensing techniques, and discusses data, the associated analysis methods, and applications. Social sensing has brought us massive amounts of spatial data related to humans. The spatio-temporal analysis of social sensing data is working for different aspects of humans’ lives, such as environment, emergency, economy, and urban planning. In the coming big data era, GIScientists should investigate theories in using social sensing data, and develop new methodologies to understand human activity based on social sensing.

Digital outcrop models provide a powerful data basis to obtain orientation information on rock masses. Robust and transferrable automatic methods are required to process and analyze these data, as outcrops and hence acquired 3D point clouds are influenced by varying conditions depending e.g., on the site, atmospheric conditions and other factors. A crucial aspect in assessing the structural character of rock masses is the analysis of fractures, which can be done directly in the 3D point cloud. This new publication validates fracture data automatically extracted from 3D rock mass data:

Drews, T., Miernik, G., Anders, K., Höfle, B., Profe, J., Emmerich, A., & Bechstädt, T. (2018). Validation of fracture data recognition in rock masses by automated plane detection in 3D point clouds. International Journal of Rock Mechanics and Mining Sciences, 109, pp. 19-31. doi: 10.1016/j.ijrmms.2018.06.023.

Drews et al. (2018), International Journal of Rock Mechanics and Mining Sciences

Preview: Drews et al. (2018), International Journal of Rock Mechanics and Mining Sciences

Abstract: This paper presents (1) an automated method to extract planes and their spatial orientation directly from 3D point clouds, followed by (2) extensive validation tests accompanied by thorough statistical analysis, and (3) a fracture intensity calculation on automatically segmented planes. For the plane extraction, a region growing segmentation algorithm controlled by several input parameters is applied to a point cloud of a granite outcrop. Within its complex surface shape, more than 1000 compass measurements were conducted for validation. In addition, digitally handpicked planes in the software Virtual Reality Geological Studio (VRGS) were used for single plane comparison. In a second test site, we performed fracture intensity calculation in Petrel based on results of the segmentation algorithm on mechanical layers of a clastic sedimentary succession. The comparison of automated segmentation results and compass measurements of three different plane sets shows a deviation of 0.70–2.00°, while the mean single plane divergence amounts to 4.97°. Hence, this study presents a fast, precise, and highly adaptable automated plane detection method, which is reproducible, transparent, objective, and provides increased accuracy in outcrops with rough and complex surfaces. Moreover, output formats of spatial orientation and location of planes are designed for simple handling in other workflows and software.

Related publication with further details on the automatic plane segmentation:

Anders, K., Hämmerle, M., Miernik, G., Drews, T., Escalona, A., Townsend, C., & Höfle, B. (2016). 3D Geological Outcrop Characterization: Automatic Detection of 3D Planes (Azimuth and Dip) Using LiDAR Point Clouds. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science, III-5, pp. 105-112. doi: 10.5194/isprs-annals-III-5-105-2016.

OpenStreetMap has become a huge source for any kind of geographic information. In OpenStreetMap you now find not only street information, but also information related to buildings, shops, sights and in Heidelberg even to individual trees.

Furthermore, OpenStreetMap data is open data – everyone is free to edit and to download the data to create own maps and analysis. In our workshop we want to explore this potential together with you.

When: Wednesday, 04.07.2018, 4 pm
Where: PC-labs + Seminarraum, Berliner Straße 48, Heidelberg

Depending on you pre-knowledge, we plan to have different groups.

Beginner:

  • no/ little knowledge about OpenStreetMap and GIS
  • download and visualize OSM data in ArcGIS or QGIS
  • easy queries and styling

Intermediate:

  • some knowledge and experience with OSM data
  • select and export only specific OSM objects
  • queries related to different timestamps, users and other parameters

Depending on the number of people interested, we will also offer a workshop for professionals with:

  • already good experience with OSM data and keen to analyse “big data”
  • store OSM data in a database
  • advanced queries for large regions containing many objects

overpass_query

Figure: Get bicycle rentals from OSM data using the overpass API

See you soon,
your disastermappers

PS: We booked the PC-labs, however it may be worth to bring the own laptop.

On Wednesday, Roderik Lindenbergh from TU Delft (NL) gave an exciting talk on robust geometry extraction in large spatial point clouds in the frame of the IWR colloquium at the Interdisciplinary Center for Scientific Computing. With focus on robust and novel methods of geoinformation extraction from these special datasets, the audience was taken on a journey through Prof. Lindenbergh’s research on Optical and Laser Remote Sensing.

Roderik Lindenbergh talking about methods for geospatial point clouds

Roderik Lindenbergh talking about methods for geospatial point cloud analysis

During the talk we learned about challenges - from easy to difficult - in 3D point cloud analysis and a variety of examples how to solve them. A particularly topical subject is the analysis of deformation from large 3D point clouds, i.e. the quantification of changes between datasets from multiple points in time. While many methods have been developed already, new challenges are emerging with evermore dense time series data becoming available.

In this context, the presentation further gave a peek into the CoastScan project of Sander Vos, Roderik Lindenbergh and Sierd de Vries on permanent laser scanning of sandy beaches. In cooperation with the team from TU Delft, beach monitoring is one of the use cases of the Auto3Dscapes research project, where the 3DGeo is developing a novel approach of autonomous 3D Earth observation based on time series of 3D point clouds.

We thank the IWR for this opportunity of meeting and discussing research and challenges in computational 3D geoinformation extraction!

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