Abstract
This paper proposed a novel observation system that is based on multi sources of collected data for urban extension detection. In addition to the satellite image processing, the evolution of unmanned aerial vehicle (UAV) technology created a practical data source for image classification and mapping. For the detected data analysis, storage and processing, a big data framework for urban extension detection was presented. In this Framework, Deep Learning (DL) algorithms were used for the classification and the analysis of multi source images.
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This work was conducted in collaboration with National Mapping and Remote Sensing Center (CNCT), in the context of the national project PRF on “Urban extension detection 2015–2018”.
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Kilani, H., Abdallah, H.B., Abdellatif, T., Attia, R. (2019). Multi-source System for Accurate Urban Extension Detection. In: El-Askary, H., Lee, S., Heggy, E., Pradhan, B. (eds) Advances in Remote Sensing and Geo Informatics Applications. CAJG 2018. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-01440-7_17
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DOI: https://doi.org/10.1007/978-3-030-01440-7_17
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