Abstract
The detection of roads from satellite images is a heated area of research in recent years. Satellite and aerial images are the most important available data sources for map generation and updating available maps. Task of automatically detecting roads is one of the specific cases of this problem. Proposed task is a difficult foresight problem because of occulations, shadows and a huge variety of non-road objects [1]. This research proposes identifying roads by means of a neural network with millions of trainable weights which sees at a much bigger context than the ones used in earlier activation functions and dropout layers. Moreover, real-time image augmentation was verified to improve the accuracy of the model and avoid overfitting. According to experiments, CNN model outperforms all the other tested methodology.
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Kanakaraddi, S.G., Chikaraddi, A.K., Pooja, B.L., Preeti, T. (2021). Detection of Roads in Satellite Images Using Deep Learning Technique. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. Lecture Notes in Networks and Systems, vol 154. Springer, Singapore. https://doi.org/10.1007/978-981-15-8354-4_44
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DOI: https://doi.org/10.1007/978-981-15-8354-4_44
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