تقرير
Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery
| العنوان: | Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery |
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| المؤلفون: | Corbane, Christina, Syrris, Vasileios, Sabo, Filip, Politis, Panagiotis, Melchiorri, Michele, Pesaresi, Martino, Soille, Pierre, Kemper, Thomas |
| المصدر: | Neural computing and Applications, 2020 |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world.The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale.This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery.A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed.The core features of the proposed model are the image patch of size 5 x 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers.The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference data-set of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. Comment: 51 pages including supplementary material, 13 Figures in the main manuscript, under review in Neural Computing and Applications journal |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/s00521-020-05449-7 |
| الوصول الحر: | http://arxiv.org/abs/2006.03267Test |
| رقم الانضمام: | edsarx.2006.03267 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/s00521-020-05449-7 |
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