Carsten Keßler

Professor

Bochum University of Applied Sciences

Germany

Research Interests

Explore related searches

Professors in Germany

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Carsten Keßler is a Professor at Bochum University of Applied Sciences in Germany. His research focuses on applied data science and urban studies, with recent contributions to the fields of population distribution modeling, historical human migration analysis, and the use of machine learning for urban analytics. His work includes exploring the FAIR Principles in Open Educational Resources and leveraging deep learning for spatial disaggregation and extreme heat exposure projections.

Articles (13)

Year:

2025

Year:

2024

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Year:

2021

Collaborators (7)

Michael Schmeltz

Assistant Professor

California State University, East Bay

UNITED STATES

View details

Jamal Jokar Arsanjani

Aalborg University

DENMARK

View details

Irma Kveladze

Aalborg University

DENMARK

View details

Peter Marcotullio

Professor

Hunter College

UNITED STATES

View details

Henning Sten Hansen

Professor

Aalborg University

DENMARK

View details

Bruno Martins

Associate Professor

Universidade de Lisboa Instituto Superior Técnico

PORTUGAL

View details

MARINA GEORGATI

Aalborg University

DENMARK

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Carsten Keßler

Research Interests

Explore related searches

Contact this professor

About

Articles (13)

Applying the FAIR Principles to Open Educational Resources: A Semantic Similarity Approach to Improve Resource Discovery

Modeling population distribution: A visual and quantitative analysis of gradient boosting and deep learning models for multi‐output spatial disaggregation

Analytics of historical human migration patterns: use cases of Amsterdam and Copenhagen

Random Forest Variable Importance Measures for Spatial Dynamics: Case Studies from Urban Demography

A deep learning method for creating globally applicable population estimates from sentinel data

Global urban exposure projections to extreme heatwaves

Investigating configurational and active centralities: The example of metropolitan Copenhagen

Machine learning for automatic detection of historic stone walls using LiDAR data

Urban Growth and Heat in Tropical Climates

The future urban heat-wave challenge in Africa: Exploratory analysis

A multi-sensor approach for characterising human-made structures by estimating area, volume and population based on sentinel data and deep learning

Geodata-driven approaches to financial inclusion – Addressing the challenge of proximity

Corrigendum to “The future urban heat-wave challenge in Africa: Exploratory analysis” [Global Environ. Change 66 (2021) 102190](S0959378020307731)(10.1016/j.gloenvcha.2020.102190)

Collaborators (7)

Michael Schmeltz

Jamal Jokar Arsanjani

Irma Kveladze

Peter Marcotullio

Henning Sten Hansen

Bruno Martins

MARINA GEORGATI

Make your next move.