DETECTION OF ROOF SURFACES FROM SATELLITE IMAGES USING ARTIFICITAL INTELLIGENCE
DOI:
https://doi.org/10.24867/30BE35GluhovicKeywords:
segmentation, training dataset, YOLOAbstract
The aim of this paper was to conduct an study in the field of detection and segmentation of roof surfaces from satellite images using various artificial intelligencebased algorithms. The paper describes the process and challenges of creating a real dataset for model training, as well as the issues related to the implementation and training of different models based on convolutional neural networks. Finally, the results of the model's predictions are presented, along with a discussion of the obtained results.
References
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[3] Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. Google Inc.
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[5] Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence.
2] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Computer Science Department and BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany.
[3] Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. Google Inc.
[4] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. University of Washington, Allen Institute for AI, Facebook AI Research.
[5] Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence.
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Published
2025-04-04
Issue
Section
Electrotechnical and Computer Engineering
