Machine-Learning-Based Classification of Urban Voids: Case Study of “Balat and Fener, Istanbul, Turkey”




urban voids, interim reuse, decision tree, machine learning, climate change mitigation


Climate change is one of the most significant challenges humanity is facing today. As major contributors to greenhouse gas emissions, cities have a crucial role in mitigating its effects. One resource that cities can leverage in this fight is their Urban Voids (UVs) - undermanaged spaces that, when adequately repurposed, can provide a range of benefits, such as supporting urban biological ecosystems and helping to address climate change. This study proposes a machine-learning-based classification of UVs in “Balat” and “Fener” in Istanbul, Turkey. By classifying UVs based on their ability to assist ecological restoration and community growth, this study offers a valuable tool for urban planners and policymakers to prioritise their efforts and spend resources more efficiently in the fight against climate change. The classification in this study is based on five factors - Ownership, Debris, Economic activity, Seal, and Leisure facilities - which were selected based on their significance in previous studies of UVs. A decision tree algorithm was employed to classify the UVs into six categories, and an artificial neural network (ANN) was used to validate the classification with an accuracy of 97%. Overall, this study offers insights into the potential of machine learning in UV classification and provides a valuable tool for urban planners and policymakers to manage and activate UVs effectively.


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How to Cite

Samadi, M., & Akcay Kavakoglu, A. (2023). Machine-Learning-Based Classification of Urban Voids: Case Study of “Balat and Fener, Istanbul, Turkey”. SPACE International Journal of Conference Proceedings , 3(1), 17–24.