Carsten Keßler

Hej! I'm

Carsten Keßler

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

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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: 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 Additionally, as it stands today, the registry of these structures, managed by each municipality, is outdated and incomplete.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. January 2022. 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.

Abstract: Several global and regional efforts have been undertaken to map human-made settlements and their characteristics, including building material, area, volume, and population. However, given the unprecedented amount of Earth observation data and processing power available, there is a timely need for developing novel approaches for mapping these characteristics at higher spatial and temporal resolution. Such information is key to effectively answering questions related to population growth, pollution, disaster management, risk assessments, spatial planning, and even generating business cases in peri-urban and rural areas. While such data is available from mapping agencies or commercial companies in some countries, there are many countries where this is not the case. The main objective of this study is to propose an Inception-ResNet inspired deep learning approach to estimate the characteristics and location of human-made structures, including estimates of population, based on Earth observation data from the Copernicus Programme. The study investigates the effects on prediction accuracy using data from different orbital directions and interferometric coherence from Sentinel 1 data and different band combinations of Sentinel 2 data as model input variables. The model is trained and evaluated on a nationwide Danish case study, where the national mapping agency provides high-quality open data on human-made structures, which serves as the ground truth data for the study. Our findings reveal that it is possible to design models that, on average, perform within 2.6% total absolute percentage error for area predictions, 7.7% for volume and 17% for population at 10 by 10 m scale using only Copernicus data and deep learning models. The models achieved 98.68% binary accuracy for extracting structural area when all test sites were merged. Combining Sentinel 1 and 2 input variables yielded the best results, while adding interferometric coherence did not significantly improve accuracy. Furthermore, including data from both orbital directions of the Sentinel 1 constellation significantly improved model performance.

Reference: Casper Samsø Fibæk, Carsten Keßler, Jamal Jokar Arsanjani (2021) A multi-sensor approach for characterising human-made structures by estimating area, volume and population based on sentinel data and deep learning. International Journal of Applied Earth Observation and Geoinformation, Volume 105, 102628.

Marcotullio, P.J., Keßler, C., Quintero Gonzalez, R., and Schmeltz, M. (2021) Urban Growth and Heat in Tropical Climates. Frontiers in Ecology and Evolution.

Abstract: This research describes the change in temperatures across approximately 270 tropical cities from 1960 to 2020 with a focus on urban warming. It associates urban growth indicators with temperature variations in tropical climate zones (tropical rainforest, tropical monsoon, and tropical wet-dry savanna). Our findings demonstrate that over time while temperatures have increased across the tropics, urban residents have experienced higher temperatures (minimum and maximum) than those living outside of cities. Moreover, in certain tropical zones, over the study period, temperatures have risen faster in urban areas than the background (non-urban) temperatures. The results also suggest that with continuing climate change and urban growth, temperatures will continue to rise at higher than background levels in tropical cities unless mitigation measures are implemented. Several fundamental characteristics of urban growth including population size, population density, infrastructure and urban land use patterns are factors associated with variations in temperatures. We find evidence that dense urban forms (compact residential and industrial developments) are associated with higher temperatures and population density is a better predictor of variation in temperatures than either urban population size or infrastructure in most tropic climate zones. Infrastructure, however, is a better predictor of temperature increases in wet-dry savanna tropical climates than population density. There are a number of potential mitigation measures available to urban managers to address heat. We focus on ecological services, but whether these services can address the projected increasing heat levels is unclear. More local research is necessary to untangle the various contributions to increasing heat in cities and evaluate whether these applications can be effective to cool tropical cities as temperature continue to rise. Our methods include combining several different datasets to identify differences in daily, seasonal, and annual maximum and minimum temperatures.

The Department of Geodesy at Bochum University of Applied Sciences is one of the co-applicants of the NFDI4Earth project which is now funded for 5 years by the German Research Foundation (DFG) to build the national research data infrastructure for the earth system sciences.


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