Carsten Keßler

Hej! I'm

Carsten Keßler

I'm a professor for geoinformatics at Bochum University of Applied Sciences and Aalborg University Copenhagen.

Home News Publications Projects CV Contact


AquaINFRA is a 4-year Horizon Europe project that aims to develop a virtual environment equipped with FAIR multi-disciplinary data and services to support marine and freshwater scientists and stakeholders restoring healthy oceans, seas, coastal and inland waters. The AquaINFRA virtual environment will enable the target stakeholders to store, share, access, analyse and process research data and other research digital objects from their own discipline, across research infrastructures, disciplines and national borders leveraging on EOSC and the other existing operational dataspaces. Besides supporting the ongoing development of the EOSC as an overarching research infrastructure, AquaINFRA is addressing the specific need for enabling researchers from the marine and freshwater communities to work and collaborate across those two domains.

Within the project, we will develop and coordinate the training and education activities for the AquaINFRA virtual environment in collaboration with similar activities in other EOSC- and RDM-related projects.

Marcotullio, P.J., Keßler, C., and Fekete, B.M. (2022) Global urban exposure projections to extreme heatwaves. Frontiers in Built Environment.

Abstract: Over the past decades, the world has experienced increasing heatwave intensity, frequency, and duration. This trend is projected to increase into the future with climate change. At the same time, the global population is also projected to increase, largely in the world’s cities. This urban growth is associated with increased heat in the urban core, compared to surrounding areas, exposing residents to both higher temperatures and more intense heatwaves than their rural counterparts. Regional studies suggest that Asia and Africa will be significantly affected. How many people may be exposed to levels of extreme heat events in the future remains unclear. Identifying the range in number of potentially exposed populations and where the vulnerable are located can help planners prioritize adaption efforts. We project the ranges of population exposed to heatwaves at varying levels to 2,100 for three future periods of time (2010–2039, 2040–2069, 2070–2099) using the Shared Socio-Economic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). We hypothesize that the largest populations that will be exposed to very warm heatwaves are located in Asia and Africa. Our projections represent the warmest heatwaves for 15 days during these three periods. By the 2070–2099 period, the exposure levels to extreme heatwaves (>42°) exceed 3.5 billion, under the sustainability scenario (RCP2.6-SSP1). The number of those exposed in cities climbs with greater projected climate change. The largest shares of the exposed populations are located in Southern Asia and tropical countries Western and Central Africa. While this research demonstrates the importance of this type of climate change event, urban decision-makers are only recently developing policies to address heat. There is an urgent need for further research in this area.

A new OA paper on Migration Studies with a Compositional Data Approach: A Case Study of Population Structure in the Capital Region of Denmark has just been published as part of the ICCSA 2022 Workshop proceedings.

Abstract: Computing percentages or proportions for removing the influence of population density has recently gained popularity, as it offers a deep insight into compositional variability. However, data are constrained to a constant sum and therefore are not independent observations, a fundamental limitation for applying standard multivariate statistical tools. Compositional Data (CoDa) techniques address the issue of standard statistical tools being insufficient for the analysis of closed data (i.e., spurious correlations, predictions outside the range, and sub-compositional incoherence) but they are not widely used in the field of population geography. Hence, in this article, we present a case study where we analyse at parish level the spatial distribution of Danes, Western migrants and non-Western migrants in the Capital region of Denmark. By applying CoDa techniques, we have been able to identify the spatial population segregation in the area and we have recognised patterns in the distribution of various demographic groups that can be used for interpreting housing prices variations. Our exercise is a basic example of the potentials of CoDa techniques which generate more robust and reliable results than standard statistical procedures in order to interpret the relations among various demographic groups. It can be further generalised to other population datasets with more complex structures.

Reference: Javier Elío, Marina Georgati, Henning Sten Hansen, Carsten Keßler (2022). Migration Studies with a Compositional Data Approach: A Case Study of Population Structure in the Capital Region of Denmark. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13379. Springer, Cham. DOI:10.1007/978-3-031-10545-6_39

A new OA paper on A deep learning method for creating globally applicable population estimates from sentinel data has just been published in Transactions in GIS.

Abstract: Recent research has shown promising results for estimating structural area, volume, and population from Sentinel 1 and 2 data at a 10 by 10-m spatial resolution. These studies were, however, conducted in homogeneous countries in Northern Europe. This study presents a deep learning methodology for population estimation in areas geographically distinct from Northern Europe. The two case study areas are Ghana and Egypt's Mediterranean coast, with supplementary ground truth data collected from Uganda, Kenya, Tanzania, Palestine, and Israel. This study aims to answer the question: How can we use Deep Learning to map structural area and type to derive population estimates for Ghana and Egypt based on Sentinel data? At 10 by 10-m resolution, the accuracy of the presented area predictions is similar to the Google Open Buildings dataset. An intercomparison of the presented population predictions is made with global state-of-the-art spatial population estimates, and the results are promising, with the proposed methodology showing comparable or better results than the state-of-the-art for the study areas.

Reference: Casper Samsø Fibæk, Carsten Keßler, Jamal Jokar Arsanjani, Marcia Luz Trillo (2022) A deep learning method for creating globally applicable population estimates from sentinel data. Transactions in GIS 26(8), pp. 3147-3175: DOI:10.1111/tgis.12971.

A new OA paper on Machine learning for automatic detection of historic stone walls using LiDAR data, led by a fantastic team of students, has just been published in International Journal of Remote Sensing.

Abstract: Stone walls in the landscape of Denmark are protected not only for their cultural and historical significance but also for their vital role in supporting local biodiversity. Many stone wall structures have either disappeared, suffered substantial damage, or had segments removed. Additionally, as it stands today, the registry of these structures, managed by each municipality, is outdated and incomplete. Leveraging recent developments in Machine Learning and Convolutional Neural Networks (CNNs), we analyze the publicly available terrain data (40 cm resolution) derived from the Danish LiDAR data, using a U-Net-like CNN model to assess the stone walls dataset and provide for an update of the registry. While the Digital Terrain Model (DTM) alone provided good results, better results were obtained when adding Height Above Terrain (HAT) and an additional DTM layer with a Sobel filter applied. Using a pixel-wise evaluation, there was an overall agreement of 93% between ground truth and prediction of stone walls in a validation area and 88% overall agreement for the whole predicted area. Good generalizability was found when externally validating the model on new data, showing positive results for both the existing stone walls and predicting new potential ones upon visualisation. The method performed best in open areas, however positive results were also seen in forested areas, although denser areas and urban areas presented as challenging. Given the lack of a reference dataset or other studies on this specific matter, the evaluation of our study was heavily based on the stone walls registry itself complemented by visual inspection of the predictions and on the ground in the Danish municipality of Ærø. Automating the process of identifying and updating the stone walls registry in Denmark is of great relevance to the local governments. We suggest the development of a Decision Support System to allow municipalities access to the results of this method.

Reference: Ezra Francis Leslie Trotter, Ana Cristina Mosebo Fernandes, Casper Samsø Fibæk, Carsten Keßler (2022) Machine learning for automatic detection of historic stone walls using LiDAR data. International Journal of Remote Sensing, 43:6, 2185-2211, DOI:10.1080/01431161.2022.2057206.

Andrakakou M, Keßler C. Investigating configurational and active centralities: The example of metropolitan Copenhagen. Environment and Planning B: Urban Analytics and City Science 47(7):1949–1966. DOI:10.1177/23998083211072861.

Abstract: Identifying centralities in cities helps determine how public space is perceived and utilized in everyday life. Sustainable mobility, social sustainability and spatial justice can be examined by investigating centralities in the urban form. In this study, we investigate configurational centralities in metropolitan Copenhagen created by the road network based on space syntax analysis and active centralities of land-use patterns with a geographical approach. The purpose of the research is to present a reproducible methodology for determining the active and configurational centralities. Using this methodology, we explore the meaning of the centralities in terms of pedestrian and cyclist accessibility, as well as the role of the configurational centralities in shaping land-use patterns. The results serve as input to an analysis of their relation through Kernel Density Correlation and spatial correlation. The results of correlations indicate that areas close to the city centre and around the Finger Plan – Copenhagen’s strategic development plan – tend to be more central and favourable for pedestrians and cyclists. On the contrary, central areas far from the city centre, especially in Northern Copenhagen, and areas between the axes of the Finger Plan are more car-oriented since centralities are dispersed and located around highways or road segments designed for cars. The workflow presented in this paper is provided as a set of open-source R scripts that draw largely on data from OpenStreetMap, thus enabling replications of the study for other cities.


Most of my publications are available for download in PDF format – just click on the title. Note that the preprints provided here may deviate slightly from the published versions. See my Google Scholar profile for an overview of where my papers have been cited.

Journal papers

Book chapters

Books and edited journal issues:

Fully reviewed conference & workshop publications

Workshop papers, posters, demos, non-reviewed conference papers & abstracts

Edited proceedings



Current and previous projects I am/have been involved in:

Short CV

Since 2021

Professor for Geoinformation Systems and Spatial Analysis, Department of Geodesy
Bochum University of Applied Sciences, Germany

Since 2020

Professor for Geoinformatics
Department of Planning
Aalborg University, Copenhagen, Denmark


Associate Professor for Geoinformatics
Department of Planning
Aalborg University, Copenhagen, Denmark


Assistant Professor for Geographic Information Science
Department of Geography
Hunter College
City University of New York, USA


Office for the Coordination of Humanitarian Affairs
United Nations, Geneva, Switzerland


Post-Doc Researcher
Institute for Geoinformatics
Westfälische Wilhelms-Universität Münster, Germany


PhD (Dr. rer. nat.) in Geoinformatics
Institute for Geoinformatics
Westfälische Wilhelms-Universität Münster, Germany