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Identifier

000440622

Title

Flood mapping via satellite remote sensing and artificial intelligence

Alternative Title

Χαρτογράφηση πλημμύρας μέσω δορυφορικής τηλεπισκόπησης και τεχνητής νοημοσύνης

Author

Δρακωνάκης, Γεώργιος Ι.

Thesis advisor

Τσακαλίδης, Παναγιώτης

Reviewer

Αργυρός, Αντώνιος
Πρατικάκης, Πολύβιος
Χρυσουλάκης, Νεκτάριος

Abstract

Countries around the world experience climate-related changing conditions which reflect severe risks to the normal and sustainable operations of modern societies. Extreme weather events, such as floods, rising sea-levels and storms, stand as characteristic examples that impair the core services of the global ecosystem. Specifically, floods depict severe impact on human societies. Climate change acts in benefit of such events, which are increasing in terms of frequency and magnitude. Early-stage and accurate delineation of the disaster is of top-priority since it provides environmental, economic, and societal benefits and eases relief efforts. Satellite imaging systems provide significant assistance towards the monitoring of natural disasters. The combination of satellite imagery with Artificial Intelligence (AI) technologies provide a strong insight, and they are able to successfully tackle the problem of flood detection and mapping. In this thesis, we introduce OmbriaNet, a deep neural network architecture, based on Convolutional Neural Networks (CNNs), that detects changes between permanent and flooded water exploiting the temporal differences among flood events extracted by different sensors. To illustrate the superior performance of our system, we construct OMBRIA, a bitemporal and multimodal satellite imagery data-set for image segmentation through supervised binary classification. It consists of a total number of 2776 images, Synthetic Aperture Radar imagery (SAR) from Sentinel-1 and multispectral imagery from Sentinel-2, accompanied with ground truth binary images produced from data derived from experts and provided from the Emergency Management Service of the European Space Agency (ESA) Copernicus Program. The dataset covers 20 flood events around the globe starting from 2017 to 2020. We collect data, co-registrate and pre-process them in Google Earth Engine. To validate the performance of our algorithm, we perform benchmarking experiments on OMBRIA dataset, with other competitive state-of-the-art techniques: the global adaptive binarization threshold algorithm (Otsu's method), traditional machine learning algorithms (i.e Support Vector Machines), and the widely recognized image segmentation U-Net deep learning architecture, that act as baseline. The performed experimental analysis that the proposed formulation is able to produce high-quality flood maps, achieving superior performance compared with the state-of-the-art.

Language

English

Subject

Deep learning

Machine learning

Μηχανική μάθηση

Issue date

2021-07-30

Collection

School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses

Type of Work--Post-graduate theses

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