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From their beginnings some 4,000 years ago to their decadence around 400 b.c., the Olmec people achieved a high level of sociopolitical complexity and dominated their native geographic territory, the southern Gulf Coast of Mexico. The first Olmec capital of San Lorenzo, Veracruz, was the only site in Mesoamerica that produced imposing monumental stone sculpture and architecture between 1800 and 1000 b.c. These characteristics reflect the capabilities of its centralized political system headed by hereditary rulers with divine legitimation. Key issues regarding the development of San Lorenzo Olmec culture center on subsistence and environment. The present study focuses on a portion of the landscape located immediately north of the first Olmec capital of San Lorenzo, Veracruz, that has been proposed as a key resource area during the development of the first civilization in Mesoamerica. We calculate the surface, volume, and water depth of this area based on archaeological data and a Digital Terrain Model (DTM) derived from an airborne Light Detection and Ranging (LiDAR) survey. The expected minimum and maximum area, local minimum altitude, and the DTM of 5-m spatial resolution provide a basis for inferences regarding the characteristics of the wetland ecosystem during Olmec times. The goal is to quantify and qualify the potential of this resource zone relying on LiDAR topography. Our models validate the observations in the field and, when combined with algorithms, they confirm the archaeological conclusions. We affirm that the northern plain in Olmec times was deeper than it is today and would have been a source of abundant aquatic resources for the primary subsistence of the early Olmec society.

Ramírez-Núñez, C., Cyphers, A., Parrot, J.-F. & Höfle, B. (2019): Multidirectional Interpolation of LiDAR Data from Southern Veracruz, Mexico: Implications for Early Olmec Subsistence. Ancient Mesoamerica. Cambridge University Press.

Three invited speakers are now joining the compact course and workshop STAP19 on Spatial and Temporal Analysis of Geographic Phenomena organized by the 3DGeo and FCGL at IWR in Heidelberg.

Dr. Gottfried Mandlburger (Institute for Photogrammetry, University of Stuttgart) will give a talk on methods of feature and information extraction from geographic 3D point clouds. He is a main developer of the point cloud processing software OPALS (GEO Department, TU Vienna). A hands-on introduction to geographic point cloud analysis with OPALS will follow up his talk.

Prof. Dr. Andreas Nüchter (Institute of Computer Science, University of Würzburg) is joining for a session on state-of-the-art methods of 3D point cloud processing. He is head of the open source project 3D-Toolkit (3DTK), a powerful package of algorithms and methods for 3D processing. A practical exercise using 3DTK will complement this session.

Jorge Martínez-Sánchez (CiTIUS, University of Santiago de Compostela) is joining STAP19 as core developer of the LiDAR simulation framework HELIOS. He will present recent developments in the software project as well as its versatile application possibilities, which will further be part of the Programming and Research Challenge of the workshop.

Details and the most recent program can be found on the STAP19 website. There are still a few spots available – register for participation until 15th February 2019!

Follow STAP19 updates on this blog and Twitter: #STAP19

STAP19 is in part supported by the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp), founded by DFG grant GSC 220 in the German Universities Excellence Initiative.

OpenMapSurfer is the name of a web tile service based on OpenStreetMap data developed by Maxim Rylov and hosted by the Heidelberg Institute for Geoinformation Technology. The map style is a general purpose “basemap” layer featuring some unique properties, such as high cartographic quality label placement, see floor bathymetry, and a pleasant warm color scheme.

Screenshot of OpenMapSurfer tiles

OpenMapSurfer was initially presented in early 2012 and has thus been online for over 7 years now. This meant that the tile server (korona.geog.uni-heidelberg.de), which had already surpassed it’s hardware’s expected lifespan by a significant margin, had to be replaced by something new. We have deployed the map styles offered by OpenMapSurfer on the brand new cloud computing environment by Heidelberg University (heiCLOUD).

The tiles can now be accessed through the openrouteservice api. Please sign up there and request an API token. Currently, requests are limited to 40 tiles per second which will be increased. If you need more than that, please send us an email to support@openrouteservice.org with your API token/key and HTTP origin (referrer) from which the requests will be made.

Wanna check out more GIScience Heidelberg OSM Web Maps? What about e.g. osm-wms.de, histosm.org, osmatrix, osmlanduse.org or the climate protection map?


Editors: R Westerholt , F-B Mocnik, A Comber, C Davies, D Burghardt, and A Zipf

A place for place – modelling and analysing platial representations

Places are understood as locations and areas to which anthropogenic meaning is ascribed. As such, places have been of central interest to philosophers and geographers for a long time, and a large stack of mostly discursive and qualitative literature evolved around this topic. Talking about digital and formal representations of places, however, the inherent vagueness of the aforementioned definition has so far hindered significant progress towards a platial notion of GIS. Place as a concept in the field of GIScience is therefore still in its infancy. Some progress has been made recently, but a consistent theory of how to characterise, represent and utilize places in formal ways is still lacking. A place-based account of GIS and analysis is nevertheless important in the light of the plethora of increasingly place-based information that we have available in an increasingly digital world. Digital technologies are nowadays strongly integrated into everyday life. As a result, a large number of especially urban datasets (e.g., geosocial media feeds, online blogs, etc.) mirror to some degree how people utilise places in subjective and idiosyncratic manners. Taking full advantage of these often user-generated datasets requires a thorough understanding of places. It also makes apparent the pressing need for respective models of representation, analytical approaches, and visualisation methods. This demand reflected by recent events like the PLATIAL’18 workshop lays out the motivation of convening this special issue.

We are seeking your original contributions on the following topics (and beyond if fitting):

  • How can we move forward the integration of platial information with GIS?
  • How can we integrate and align GIScience notions of place with existing human-geographic and philosophical notions?
  • How is it possible to establish and quantify relationships between adjacent places?
  • What might be a suitable strategy for aggregating subjective platial information?
  • What roles do uncertainty and fuzziness take in a platial theory of geoinformation?
  • In which ways can places be visualised, and how can we do that at multiple scales?
  • What can we learn about places from volunteered and ambient geographic information?
  • How can platial analysis be integrated with applied research agendas from neighbouring disciplines like sociology, urban planning, or human geography?
  • Further topics are welcome if they fit the overall theme of this special issue
Important dates and anticipated timeline:

30 March 2019

Deadline: Full paper submission

15 June 2019

Anticipated paper acceptance notification

1 July 2019

Camera-ready papers are due

1 August (anticipated)

Publication of the special issue

Further Info (pdf)

As part of the lecture “Geodatenerfassung”, geography students in their first semester turned theory of digital geodata acquisition into practice. Based on their aims and requirements, the students acquired geodata with different methods and sensors. Using a high-end Riegl VZ-2000i terrestrial laser scanner, two groups captured the old town of Heidelberg and a limestone quarry as 3D point clouds and thereby learnt how to define a proper scan setup according to their geographical application. Other groups used their smart phones for noise mapping, the creation of photogrammetric models of a church or the evaluation of the quality of cycle paths in Heidelberg.

The objective of this practical exercise is to get in touch with latest methods, sensors and concepts for the acquisition of digital geodata, since this is an essential part of geographic work in science and practice. For individual geodata acquisition methods (e.g. user-generated geodata on the web or 3D laser scanning), the GIScience and 3DGeo research groups regularly offer special courses for further in-depth training.

You might be interested in reading about latest teaching activities in Morocco or Austria in our GIScience News Blog.

Auch dieses Jahr geht das Ausstellungsschiff MS Wissenschaft auf Tour. Im Fokus steht das Thema Künstliche Intelligenz (KI). Mit an Bord das Exponat „Micro-Mapping“, das wir am HeiGIT in Kooperation mit dem Alfred-Wegener-Institut erstellen.

Unser Exponat stellt die Bedeutung des Menschen, besser gesagt: einer Menschenmenge („Crowd“), bei der Entwicklung von KI in den Vordergrund. Es wird erläutert wie Freiwillige durch visuelle Interpretation von Fernerkundungsbildern der Maschine dabei helfen komplexe Erkennungsmuster zu erlernen. Die Besucher/-innen können selbst Trainingsdaten für maschinelles Lernen spielerisch in Sekundenschnelle mit Hilfe von „Micro-Mapping“ generieren. Im Mittelpunkt stehen dabei zwei Anwendungsbeispiele aus den Geo- und Klimawissenschaften: Das „Mappen“ von Gebäuden, sowie das Erkennen von Landoberflächenstrukturen in arktischen Permafrostregionen.

Weiterführende Information zum „Micro-Mapping” + KI:

Porto de Albuquerque, J., B. Herfort, M. Eckle (2016): The Tasks of the Crowd: A Typology of Tasks in Geographic Information Crowdsourcing and a Case Study in Humanitarian Mapping. Remote Sensing. 2016, 8(10), 859; doi:10.3390/rs8100859.

Herfort, B., Höfle, B. & Klonner, C. (2018): 3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 137, pp. 73 -83.

Chen, J., Y. Zhou, A. Zipf and H. Fan (2018): Deep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping. IEEE Transactions on Geoscience and Remote Sensing (TGRS). 1-10. https://doi.org/10.1109/TGRS.2018.2868748

A series of video tutorials of GigaMesh has recently become available on YouTube. GigaMesh is a software framework for displaying, processing and visualizing large meshes of 3D spatial data representing 2D surfaces.

The most recent tutorial by Paul Bayer and Hubert Mara presents a rapid method of hillshading for a geospatial dataset. The video shows the powerful 3D spatial visualization methods implemented in the software in a geographic context using HiRISE terrain data from Mars.

Check out the video here: https://youtu.be/GwhC7mGWY-A

Hillshading in GigaMesh

Hillshading in GigaMesh

Want to try it yourself? Download pre-built Linux binaries and packages of GigaMesh together with example data.

Follow latest news and updates on ResearchGate.

GigaMesh is developed within the Forensic Computational Geometry Laboratory (FCGL) of Dr. Hubert Mara at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University.

Machine Learning for Space and Earth Observation Data (ML-SEOD) 2019

Call for Papers

The Earth and Space environments are being monitored by an unprecedented amount of sensors: Earth observation satellites, sensor networks, telescopes working in different wavelengths, human records of Earth and Space events, etc. This generates a huge amount of raw data that must be processed in order to yield insights and knowledge about these environments.

Data can be as varied as the sensors and applications for it, ranging from well-structured time-series of spatial data such as data cubes, to spectra in different resolutions from objects in the sky, to semi-structured data collected from different types of sensor networks. Even social networks can be used to monitor reports on Earth and Space phenomena.

Machine learning methods can be used to process Earth and Space data in different ways: to extract knowledge from data, to transform data in different representations, to search for patterns on data, to compare data to models or other observational data, etc. This workshop will address the different practical aspects of application of machine learning technologies to data related to the Earth and Space environments, covering algorithms, methodologies, applications and case studies.

Topics of Interest

The workshop welcomes contributions on applications of machine learning for Environmental and Space Data, including (but not limited to):

  • Machine learning applications to Remote Sensing and Earth Observation data (image segmentation and classification, time series clustering and classification, spatio-temporal data analysis, etc.);
  • Machine learning applications to Space Observation data (image processing and classification, spectra classification, time series analysis and classification, etc.);
  • Volunteered Geographic Information (VGI) and citizen engagement in machine learning models for space and Earth observation data.
  • Novel algorithms for machine learning and applications, and different approaches and uses of classical algorithms;
  • HPC for machine learning for Environmental and Space Data;
  • Environmental and Space Observation Data representation, storage and retrieval for computing-intensive machine learning;
  • Data intensive computing applied to Earth and Space Observation data in general;
  • Case studies and Experiences.

Important Deadlines

  • Paper submission: March 17th, 2019
  • Notification of Acceptance: March 31st, 2019
  • End of Early-bird Registration: May 8th, 2019
  • Camera Ready Submission: May 8th, 2019
  • ICCSA 2019 Conference: July 1st-4th, 2019


To submit a paper, please connect to the Submission site from the link available at the ICCSA 2019 web site. Only papers submitted through the electronic system and strictly adhering to the relevant format will be considered for reviewing and publication. The paper must deal with original and unpublished work, not submitted for publication elsewhere. All submissions will be reviewed by at least three experts in the relevant field. The submitted paper must be camera-ready, between 10 and 16 pages long and formatted according to the LNCS rules. Please consult the formatting information and templates. Please pay attention, when submitting your contribution, to select the entry Machine Learning for Space and Earth Observation Data (ML-SEOD) 2019 in the listbox shown in the submission form.


Accepted and presented papers will be published as part of the conference proceedings by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) Series.

Organising Committee

  • Rafael Santos, National Institute for Space Research, Brazil
  • Karine Reis Ferreira, National Institute for Space Research, Brazil

Programme Committee (Confirmed)

  • Thales Sehn Körting (National Institute for Space Research, Brazil)
  • Victor Maus (Vienna University of Economics and Business, Austria)
  • Tessio Novack (Heidelberg University, GIScience Research Group, Germany)
  • João Pires (UNL, Portugal)
  • Ádamo Santana (Fuji Electric, Japan)
  • Alexander Zipf (Heidelberg Institute for Geoinformation Technology, Germany)

The most optimal route through Germany's 15 biggest citys. It's the shortest route of exactly 43.589.145.600 different alterations.

In a Vehicle Routing Problem (VRP, an example is the Traveling Salesman Problem), we are concerned with finding optimal routes for a fleet of vehicles having to reach given destinations, e.g. in order to deliver goods to customers. Due to its high computational complexity, this task requires dedicated VRP solvers, such as VROOM. In collaboration with the VROOM project the openrouteservice (ORS) team at HeiGIT contributed a ORS routing backend to the VROOM software. Now, VROOM is able to optimize vehicle routes based on shortest paths computed by ORS using OpenStreetMap data. This allows to solve several Traveling Salesman Problems for vehicle fleets, This offers many usage possibilities in lot of logistics and traffic applications.

A dedicated VROOM API will soon be available via openrouteservice. Stay tuned for further updates! This adds to the already remarkable list of the ORS API features including

  • routing with directions for all different kinds of bicycle profiles, pedestrian, wheelchair, car and heavy vehicle
  • time-distance matrices
  • geocoding and reverse geocoding
  • isochrones for reachability applications
  • points of interest
  • elevation information for points and or linestrings

plus a growing ecosystem of routing libraries for different languages such as python, R stats, JavaScript or a QGIS plugin. Find the sources on GitHub/GIScience. See also our related research on healthy, quiet and green routing, wheelchair accessibility or Landmark navigation or routing through open spaces etc.

we want to remind you about the extended submission deadline (15 March 2019) for the Special Issue “Volunteered Geographic Information: Analysis, Integration, Vision, Engagement (VGI-ALIVE)”of the ISPRS International Journal of Geo-Information (IJGI)

The steady rise of data volume shared on already-established and new Volunteered Geographic Information (VGI) and social media platforms calls for advanced analysis methods of user contribution patterns, leads to continued challenges in data fusion, and provides also new opportunities for rapid data analysis for event detection and VGI data quality assessment. Questions regarding the future of VGI and social media platforms include the prospect of continued user growth, engagement of new user groups, further expansion of VGI to educational activities, and closing data gaps in geographically underrepresented areas.

The SI evolves around a wide range of VGI and social/media research topics including cross-platform data contributions, innovative VGI analysis approaches, current data fusion methods, data interoperability, real-world applications, and the use of VGI and social media use in education. Contributions that discuss future challenges of VGI and social media, may it be on the legal or technical side, that formulate a vision for VGI and social media usage and analysis for the near future, and that demonstrate analysis workflows or the integration of VGI into education are also welcome. This Special Issue offers an outlet for publishing papers relevant to the scope of the related AGILE workshop. Papers will be reviewed on a continuing basis until the submission deadline.

Special issue topics include (but are not limited to):

  • Activity patterns and collaboration across multiple VGI and social media platforms
  • (Quasi) real-time analysis of VGI and social media content
  • Technical and legal aspects of crowd-sourced data fusion
  • Opportunities, challenges, and limitations for the future of VGI
  • VGI and social media analysis in geographic areas with sparse data coverage
  • Novel methods of VGI data quality assessment
  • Mobility patterns from VGI and social media
  • User engagement and VGI education
  • Closing the gaps in VGI data coverage

Dr. Peter Mooney
Dr. Franz-Benjamin Mocnik
Prof. Dr. Alexander Zipf
Dr. Jamal Jokar Arsanjani
Dr. Hartwig H. Hochmair
Ms. Kiran Zahra

Guest Editors


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