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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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.

We have presented two short papers this week at AGILE 2021:

Georgati, M. and Keßler, C.: Spatially Explicit Population Projections: The case of Copenhagen, Denmark, AGILE GIScience Ser., 2, 28,, 2021

Abstract: Cities expand rapidly with international migration significantly contributing to urban growth and urban population change. However, cities miss out on a great opportunity of reclaiming valuable knowledge on future population distribution due to the lack of established tools and methodologies to project where it is more likely for people of specific socio-demographic groups to set up home. The present work suggests that spatially explicit projections can play a significant role as a tool for urban planning and for managing diversity creatively, especially when a combination of social, demographic and topographic data is utilized. Machine learning techniques have demonstrated capabilities to capture relationships among this plethora of urban features to estimate future population distribution. We present a flexible, ML-based methodology for high-resolution gridded population projections by demographic characteristics, and specifically by region of origin, for the capital region of Copenhagen, Denmark, by combining various socio-demographic and topographic input layers.

Michalak, P., Patsili, A., Carmen, O., and Keßler, C.: Impact of sea level rise on the land cover structure in Southeast Asia, AGILE GIScience Ser., 2, 36,, 2021.

Abstract: Sea-level rise in Southeast Asia is a consequence of climate change that will affect almost all coastal countries in the region. The results of this phenomenon may have severe consequences, from problems with food production, through mass migration of people, to the threat to unique ecological areas. Hence, the main aim of this research was to investigate the impact of sea level rise on the land cover structure in the region and how it may affect the situation of the countries in the region. For this purpose, GlobCover 2009 data and projections of sea level rise by one meter were used and a multiband raster image was created containing information about the land cover class, country and whether the area is threatened by sea level rise. All calculations have been made on the raster prepared in this way, which shows that 4.4% of South East Asia's areas are at risk of rising sea levels. Finally, the ratio was calculated for each land cover class. This showed the unusual vulnerability of some of the classes to rising sea levels like irrigated croplands and urban areas.

Our paper on Geodata-driven approaches to financial inclusion – Addressing the challenge of proximity has just been published in the International Journal of Applied Earth Observation and Geoinformation (Open Access).

Abstract: Financial Inclusion is, in many ways, a spatial planning issue: Where do financial institutions provide services, how far do customers travel to access mobile money, which services are available where and how is agent cash-flow handled? Utilising geodata can contribute significantly to measuring financial access and thus assist in improving Financial Inclusion by expanding the reach of services and locating areas of economic exclusion. This study presents a new Spatial Decision Support System, with a frontend embedded directly within a spreadsheet interface, that enables measuring and planning financial access through geospatial analysis and Earth Observation derived products. The purpose is to complement existing Financial Inclusion measures, which rely significantly on large-scale representative household surveys to quantify financial access and opportunity to proxy quality of inclusion. The Decision Support System relies on Earth Observation and Public Participatory GIS, which enables a decoupling from the census cycle and global reach. Our findings indicate that a geospatial approach to measuring and making decisions regarding the location of financial access points can positively affect both tracking and delivering Financial Inclusion and reducing the urban–rural service cliff-edge. Our proposed geospatial methodology is useful for decision-makers in two ways: a) It allows the measurement of the large-scale geospatial reach of financial services – useful for decision-makers, planners, politicians, national statistical offices, and NGOs in charge of tracking progress towards the Sustainable Development Goals. b) It helps with planning and optimising services for local financial entities such as mobile money agents, brick and mortar bank branches, and less formal saving mechanisms such as saving clubs. The Spatial Decision Support System is currently used by several Financial Service Providers in Ghana and undergoing implementation for one in North-western Tanzania.

Reference: Casper Samsø Fibæk, Hanna Laufer, Carsten Keßler, Jamal Jokar Arsanjani (2021) Geodata-driven approaches to financial inclusion – Addressing the challenge of proximity. International Journal of Applied Earth Observation and Geoinformation, Volume 99, July 2021, 102325. DOI:10.1016/j.jag.2021.102325

As of April 2021, I'm joining the Department of Geodesy at Bochum University of Applied Sciences as Professor for Geoinformation Systems and Spatial Analysis. While this will be my main affiliation from now on, I will also keep my affiliation with Aalborg University Copenhagen.


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