Abstract
It is necessary to analyze the accessibility of flood-affected regions for better planning of response. Flood level detection using remotely sensed images could minimize costs and can also allow taking adequate preparation for fast recovery. MediaEval 2017 multimedia satellite task dataset is used for flood detection in satellite images. The image segmentation technique on satellite images using the modified UNET convolutional neural network is applied after pre-processing to analyze flood-affected regions and compared them to the corresponding segmented regions during the period of a flood event. Initially, all images were segmented using conventional segmentation methods like the Mean shift clustering algorithm. It is observed that the U-Net model produced a good measure for Intersection over Union (IoU) as 99.46% with 99.41% accuracy. The segmented images are then considered for further conventional processing to get meaningful information of flooded regions from satellite imagery.
Supported by Department of Science and Technology, Science and Engineering Research Board, Government of India (DST-SERB).
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References
Nogueira, K., et al.: Data-driven flood detection using neural networks. In: MediaEval 2017, Dublin, Ireland (2017)
Said, N., et al.: Deep learning approaches for flood. In: MediaEval 2018, Sophia Antipolis, France (2018)
Lopez-Fuentes, L., van de Weijer, J., Bolaños, M., Skinnemoen, H.: Multi-modal deep learning approach for flood detection. In: MediaEval 2017, Dublin, Ireland (2017)
Bischke, B., Bhardwaj, P., Gautam, A., Helber, P., Borth, D., Dengel, A.: Detection of flooding events in social multimedia and satellite. In: MediaEval 2017, Dublin, Ireland (2017)
Zaffaroni, M., Rossi, C.: Water segmentation with deep learning models for flood detection. In: Proceedings of the 17th ISCRAM Conference, VA, USA (2020)
Ahmad, K., Konstantin, P., Riegler, M., Conci, N., Holversen, P.: CNN and GAN based satellite and social media data fusion for. In: MediaEval 2017, Dublin, Ireland (2017)
Bischke, B., Helber, P., Christian, S., Srinivasan, V., Dengel, A., Borth, D.: The multimedia satellite task at MediaEval 2017. In: MediaEval 2017, Dublin, Ireland (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Mark, S., Andrew, H., Menglong, Z., Andrey, Z., Liang-Chieh, C.: MobileNetV 2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Dias, D., Dias, U.: Flood detection from social multimedia and satellite images using ensemble and transfer learning with CNN architectures. In: MediaEval 2018, Sophia Antipolis, France (2018)
Lopez-Fuentes, L., Farasin, A., Skinnemoen, H., Garza, P.: Deep Learning models for passability detection of flooded roads. In: MediaEval 2018, Sophia Antipolis, France (2018)
Kirchknopf, A., Slijepcevic, D., Zeppelzauer, M., Seidl, M.: Detection of road passability from social media and satellite images. In: MediaEval 2018, Sophia Antipolis, France (2018)
Said, N., et al.: Deep learning approaches for flood classification and flood aftermath detection. In: MediaEval 2018, Sophia Antipolis, France (2018)
Gerald, W.J., Bisshop, W., Senarath, U., Stewart, A.: Methodology for mapping change in woody landcover over Queensland from 1999 to 2001 using Landsat ETM+ (2002)
Ogashawara, I., Curtarelli, M.P., Ferreira, C.M.: The use of optical remote sensing for mapping flooded areas (2013)
Bischke, B., Borth, D., Schulze, C., Denge, A.: Contextual enrichment of remote-sensed events with social media streams. In: Proceedings of the 24th ACM International Conference on Multimedia (MM 2016). Association for Computing Machinery, New York (2016)
Sébastien, O.: Building segmentation on satellite images (2018)
Ticehurst, C.J., Dyce, P., Guerschman, J.P.: Using passive microwave and optical remote sensing to monitor flood inundation in support of hydrologic modelling. In: 18th World IMACS/MODSIM Congress, Congress, Cairns, Australia (2009)
Klemas, V.: Remote sensing of floods and flood-prone areas: an overview. J. Coastal Res. J31(4), 1005–1013 (2015)
Schnebele, E., Cervone, G., Kumar, S., Waters, N.: Real time estimation of the Calgary floods using limited remote sensing data. Water 6(2), 381–398 (2014)
Liebrecht, C.: PDF hosted at the Radboud Repository of the Radboud University Nijmegen the perfect solution for detecting sarcasm in tweets# not. In: Proceedings of the 4th Workshop on Computer Approaches to Subject Sentiment Social Media Analysis (2014)
Ahmed, A., Sinnappan, S.: The role of social media during Queensland floods: an empirical investigation on the existence of multiple communities of practice (MCoPs). Pac. Asia J. Assoc. Inf. Syst. 5(2), 2 (2013)
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Jaisakthi, S.M., Dhanya, P.R., Jitesh Kumar, S. (2021). Detection of Flooded Regions from Satellite Images Using Modified UNET. In: Krishnamurthy, V., Jaganathan, S., Rajaram, K., Shunmuganathan, S. (eds) Computational Intelligence in Data Science. ICCIDS 2021. IFIP Advances in Information and Communication Technology, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-030-92600-7_16
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