Open GIScience PostDoc positions on understanding the relationships between “Urban nature experience, biodiversity and mental health”

We call for applications to postdoctoral positions within the Heidelberg Mannheim Health and Life Science Alliance “Innovation Campus” for Inter-institutional project.

The Central Institute of Mental Health (ZI), Prof. A. Meyer-Lindenberg, the GIScience Research Group at Heidelberg University (Prof. A. Zipf), the 3DGeo Group (Prof. B. Höfle); and the Department of Biodiversity and Plant Systematics (Prof. A. Koch) at Heidelberg University propose a joint research project, that shall investigate the relationship between “Urban nature experience, biodiversity and mental health”
(Project Proposal. Nr. 55 (page 154ff)

If you are a PostDoc in the mentioned fields and interested in such work, please do apply for this project according to the details given here, referring to project Nr 55 and the respective PI:

Call for “Inter-Institutional Postdoctoral Positions” of the “Innovation Campus Heidelberg Mannheim Health &Life Sciences”:
(In addition you may also want to inform the relevant PI, e.g. Prof Zipf (GIScience) or Prof Höfle (3DGeo) about your application by email.)

Deadline for your applications is 22 September 2022, 5 pm CET.

Below you find a short summary of the project idea (Proposal No 55):

Environmental influences in urban areas pose risk to mental health (Lederbogen et al., 2011; Tost et al., 2015), making the identification and promotion of protective factors a high priority for urban public mental health and policy making. Urban nature experience and biodiversity are epidemiologically proven protective factors for mental health (Tost et al., 2019), but the daily-life psychological and neural mechanisms are poorly understood. The aim of this interdisciplinary research platform is the identification, quantification and neurobehavioral mechanistic inquiry of the causal subcomponents of nature experience and biodiversity as urban protective factors. For this purpose, the research platform strategically combines the psychiatric neuroscience research line of the Central Institute of Mental Health, Mannheim, with the research lines of the departments of GIScience and Geoinformatics and the department of Biodiversity and Plant Systematics at Heidelberg University. Extending our previous ecological neuroscience work we will 1) apply established geoinformatics and genomic approaches to quantify real-life urban environmental exposures including urban green space and biodiversity in the living environments of study participants, 2) record aspects of momentary psychological well-being (e.g., affective valence, stress experiences) with e-diaries while 500 adolescents and adults from the Rhine-Neckar metropolitan region go about their daily routines, thereby continuously collecting sensor data (e.g., geographic position, physical activity, heart rate, ambient noise, temperature) and, 3) examine brain function and established neural markers of environmental risk and protective factors of participants using functional magnetic resonance imaging. The acquired data will be analyzed in a multimodal transdisciplinary approach including stakeholders to quantify individual exposures to urban green space and biodiversity in everyday life, demonstrate protective effects of urban nature experience and biodiversity on well-being and mental health, and establish a direct mechanistic link to neural processing of environmental stimuli. In addition to providing critical basic knowledge for the development of new therapeutic and preventive programs for mental health, this transdisciplinary research platform will inform policy decisions on the design and maintenance of urban ecosystems for improving mental health and establish the Heidelberg Mannheim region as an internationally visible research area for the mental health consequences of rapidly changing biodiversity.

In this project the PostDoc at the GIScience Research Group at Heidelberg University will generate new geographical and environmental information (e.g., high resolution land use maps) through the combination and fusion of crowdsourced geodata, remote sensing imagery and laser scanning (LIDAR) as well as data from the Social Web and further public and government sources using state of the art machine learning methods. Here we integrate heterogeneous spatial data from public, private, and crowdsourced sources to calculate high resolution spatial databases with relevant attributes for environmental (e.g., noise, air quality, green space, biodiversity) and socioeconomic factors. For the proposed project we will derive a high-quality biodiversity dataset for the study area from those heterogeneous sources, including ambulatory assessments on how people roam and perceive this biodiversity. To produce this map, the heterogeneous spatial information from the different data sources (local administration, private sector, academia, crowdsourcing, Citizen Science) needs to be georeferenced, quality controlled, and spatially disaggregated towards a common spatial reference system. We use machine learning methods for data fusion and prediction of relevant biodiversity classes. The effects of this information for mental health are then calculated and spatially correlated with subject data collected by project partners.

Highly precise 3D data are particularly suited to derive a detailed description of the urban landscapes and vegetation, including proxies for biodiversity and also in-situ perception of the physical environment. For this the 3DGeo Research Group Heidelberg investigates and develops novel computational methods for the geographic analysis of 3D/4D point clouds. Our datasets are acquired by cutting-edge Earth observation technology (e.g., laser scanning, photogrammetry and radar). We aim at increasing the understanding of geographic phenomena and human-environment interactions by observing and analyzing them in full 3D, in near real-time with high spatial and temporal resolution.

Earlier related work at GIScience Heidelberg:


meinGrün – information and navigation on urban green spaces in cities
Psychogeography – Psychoepidemiology and HealthGIS in the Metropole Region Rhein-Neckar (Psychoepidemiologisches Zentrum PEZ @ ZI Mannheim)

Related earlier Publications at GIScience & 3DGeo Heidelberg University:

  • Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. (2021): Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. ISPRS Int. J. Geo-Inf. 2021, 10, 251.
  • C. Geiss, E. Brzoska, P. Aravena Pelizari, S. Lautenbach, H. Taubenböck (2022): Multi- target Regressor Chains with Repetitive Permutation Scheme for Characterization of Built Environments with Remote Sensing, International Journal of Applied Earth Observations and Geoinformation, 106, .
  • H. Lee, B. Seo, A. F. Cord, M. Volk, S. Lautenbach (2022), Using crowdsourced images to study selected cultural ecosystem services and their relationships with species richness and carbon sequestration, Ecosystem Services, 54,2022,
  • Li, H. J. Zech, C. Ludwig, S. Fendrich, A. Shapiro, M. Schultz, A. Zipf (2021): Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning.. International Journal of Applied Earth Observation and Geoinformation, Vol 104, 2021, 102571.
  • Ludwig, C.; Fendrich, S.; Zipf, A. (2020): Regional variations of context‐based association rules in OpenStreetMap. Transactions in GIS. Wiley.
  • Reichert, M., Brüßler, S., Reinhard, I. et al. The association of stress and physical activity: Mind the ecological fallacy. Ger J Exerc Sport Res 52, 282–289 (2022).
  • Reichert M., Giurgiu M., Koch E., Wieland L. M., Lautenbach S., Neubauer A. B., von Haaren-Mack B., Schilling R., Timm I., Notthoff N., Marzi I., Hill H., Brüßler S., Eckert T., Fiedler J., Burchartz A., Anedda B., Wunsch K., Gerber M., Jekauc D., Woll A., Dunton G. F., Kanning M., Nigg C. R., Ebner-Priemer U., Liao Y. (2020): Ambulatory assessment for physical activity research: State of the science, best practices and future directions, Psychology of Sport & Exercise,
  • Reichert, M.; Braun, U.; Gan, G.; Reinhard, I.; Giurgiu, M.; Ma, R.; Zang, Z.; Hennig, O.; Koch, E. D.; Wieland, L.; Schweiger, J.; Inta, D.; Hoell, A.; Akdeniz, C.; Zipf, A.; Ebner-Priemer, U.W.; Tost, H.; Meyer-Lindenberg, A.(2020): A neural mechanism for affective well-being: Subgenual cingulate cortex mediates real-life effects of nonexercise activity on energy. Science Advances.
  • Reichert, M., Braun, U., Lautenbach, S., Zipf, A., Ebner-Priemer, U., Tost, H., Meyer-Lindenberg, A. (2020): Studying the impact of built environments on human mental health in everyday life: methodological developments, state-of-the-art and technological frontiers. Current Opinion in Psychology 32, 158-164.
  • Tost, H., Reichert, M., Braun, U., Reinhard, I., Peters, R., Lautenbach, S., Hoell, A., Schwarz, E., Ebner-Priemer, U., Zipf, A., Meyer-Lindenberg, A., 2019. Neural correlates of individual differences in affective benefit of real-life urban green space exposure. Nature Neuroscience.
  • Reichert, M., Tost, H., Reinhard, I., Schlotz, W., Zipf, A., Salize, H.-J., Meyer-Lindenberg, A., & Ebner-Priemer, U. W. (2017). Exercise vs. non-exercise activity: e-diaries unravel distinct effects on mood. Medicine & Science in Sports & Exercise, 49(4): 763-773.
  • Reichert, M., Tost, H., Reinhard, I., Zipf, A., Salize, H., Meyer-Lindenberg, A., Ebner-Priemer, U.W. (2016): Within-subject associations between mood dimensions and non-exercise activity: An ambulatory assessment approach using repeated real-time and objective data. Frontiers in Psychology. 7:918. DOI:10.3389/fpsyg.2016.00918
  • Li, H.; Ghamisi, P.; Rasti, B.; Wu, Z.; Shapiro, A.; Schultz, M.; Zipf, A. A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks. Remote Sensing. 2020, 12, 2067. DOI:
  • Törnros, T., Dorn, H., Reichert, M., Ebner-Priemer, U., Salize, H.-J., Tost, H., Meyer-Lindenberg, A., Zipf, A. (2016): A comparison of temporal and location-based sampling strategies for GPS-triggered electronic diaries.” Geospatial Health. Vol 11, No 3. DOI:10.4081/gh.2016.473.
  • Reichert, M., Törnros, T., Hoell, A., Dorn, H., Tost, H., Salize, H.-J., Meyer-Lindenberg, A., Zipf, A., Ebner-Priemer, U. W. (2016). Using Ambulatory Assessment for experience sampling and the mapping of environmental risk factors in everyday life. Die Psychiatrie. 2/2016. 94-102.
  • 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
  • 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.
  • Dorn, H., Törnros, T., Reichert, M., Salize, H.J., Tost, H., Ebner-Priemer, U., Meyer-Lindenberg, A., Zipf, A. (2015): Incorporating Land Use in a Spatiotemporal Trigger for Ecological Momentary Assessments. In: Car, A., Jekel, T., Strobl, J., Griesebner, G. (Eds.), GI_Forum 2015 – Geospatial Minds for Society (pp. 113-116). Journal for Geographic Information Science, 1.
  • Foshag, K., Aeschbach, N., Höfle, B., Winkler, R., Siegmund, A. & Aeschbach, W. (2020): Viability of public spaces in cities under increasing heat: A transdisciplinary approach. Sustainable Cities and Society 59, pp. 1-10. DOI:
  • Antonova, S., Thiel, C., Höfle, B., Anders, K., Helm, V., Zwieback, S., Marx, S., Boike, J. (2019): Estimating tree height from TanDEM-X data at the northwestern Canadian treeline. Remote Sensing of Environment, 231.
  • Yan, Y., Schultz, M., Zipf, A. (2019): An exploratory analysis of usability of Flickr tags for land use/land cover attribution, Geo-spatial Information Science (GSIS), Taylor & Francis.
  • 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., (eds) OpenStreetMap in GIScience: experiences, research, applications. ISBN:978-3-319-14279-1, pp. 37-51, Springer Press.
  • Lee, H., Seo, B., Koellner, T., Lautenbach, S. (2019): Mapping cultural ecosystem services 2.0 – potential and shortcomings from unlabeled crowd sourced images. Ecol. Indic. 96, 505–515.
  • Schultz, M., Voss, J., Auer, M., Carter, S., and Zipf, A. (2017): Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation, 63, pp. 206-213. DOI: 10.1016/j.jag.2017.07.014.
  • Li, H. & Zipf, A. (2022): A conceptual model for converting openstreetmap contribution to geospatial machine learning training data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 253–259. DOI:10.5194/isprs-archives-XLIII-B4-2022-253-2022
  • Ludwig, C.; S. Lautenbach, W.-M. Schömann & A. Zipf (2021): Comparison of simulated fast and green routes for cyclists and pedestrians. 11th International Conference on Geographic Information Science (GIScience 2021). K. Janowicz & J. Verstegen (eds.); pp. 3:1–3:15. DOI:10.4230/LIPIcs.GIScience.2021.II.3
  • T. Václavík, S. Lautenbach, T. Kuemmerle, R. Seppelt (2019): Mapping Land System Archetypes to Understand Drivers of Ecosystem Service Risks, in Atlas of Ecosystem ServicesDrivers, Risks, and Societal Responses, Schröter, M., Bonn, A., Klotz, S., Seppelt, R., Baessler, C. (Eds.), Springer, 69-85,