Supervisor: Sebastian Zambanini
The INDIGO project aims to document and analyze the graffiti along Vienna’s Donaukanal. One of the main problems faced is monitoring new graffiti. Instead of solely relying on Instagram and human memory, an automatic change detection between images from different time stamps can support the monitoring process.
The goal of this work is to explore and examine methods that can be used to automatically detect changes in graffiti images.
Literature Review – getting to know the methods
Written Report/Thesis and final presentation
 M. Mandal and S. K. Vipparthi, “An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs”, in IEEE Transactions on Intelligent Transportation Systems, 23(7): 6101-6122, 2022, https://doi.org/10.1109/TITS.2021.3077883.
 Jiang, H.; Peng, M.; Zhong, Y.; Xie, H.; Hao, Z.; Lin, J.; Ma, X.; Hu, X. “A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images”, Remote Sensing 14:1552, 2022, https://doi.org/10.3390/rs14071552