A Neural Network Approach to Flood Mapping Using Satellite Imagery

This paper presents a new approach to flood mapping using satellite synthetic-aperture radar (SAR) images that is based on intelligent techniques. In particular, we apply artificial neural networks, self-organizing Kohonen's maps (SOMs), for SAR image segmentation and classification. Our approach was used to process data from different satellite SAR instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSAT-1) for different flood events: the Tisza river, Ukraine and Hungary, 2001; the Huaihe river, China, 2007; the Mekong river, Thailand and Laos, 2008; and the Koshi river, India and Nepal, 2008

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

Reputation-based secyrity for heterogtneous structurally complex systems

In this paper we present the service-oriented infrastructure within the Wide Area Grid (WAG) project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites (CEOS). The study focuses on enabling trust for this infrastructure using certificates and reputation-based model.В даній роботі розглянуто сервіс-орієнтовану інфраструктуру обробки супутникових даних, яка розроблена в межах міжнародного проекту Wide Area Grid (WAG) робочої групи WGISS комітету супутникових спостережень CEOS. Розглянуто питання забезпечення довіри в таких структурно-складних системах, зокрема на основі сертифікатів та репутації

A Utility-Based Reputation Model for Grid Resource Management System

In this paper we propose extensions to the existing utility-based reputation model for virtual organizations (VOs) in grids, and present a novel approach for integrating reputation into grid resource management system. The proposed extensions include: incorporation of statistical model of user behaviour (SMUB) to assess user reputation; a new approach for assigning initial reputation to a new entity in a VO; capturing alliance between consumer and resource; time decay and score functions. The addition of the SMUB model provides robustness and dynamics to the user reputation model comparing to the policy-based user reputation model in terms of adapting to user actions. We consider a problem of integrating reputation into grid scheduler as a multi-criteria optimization problem. A non-linear trade-off scheme is applied to form a composition of partial criteria to provide a single objective function. The advantage of using such a scheme is that it provides a Pareto-optimal solution partially satisfying criteria with corresponding weights. Experiments were run to evaluate performance of the model in terms of resource management using data collected within the EGEE Grid-Observatory project. Results of simulations showed that on average a 45 % gain in performance can be achieved when using a reputation-based resource scheduling algorithm

A review of the internet of floods : near real-time detection of a flood event and its impact

Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future

Envisat/ASAR Images for the Calibration of Wind Drag Action in the Donana Wetlands 2D Hydrodynamic Model

Donana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Donana's topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Donana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Donana's basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessment. Doñana National Park wetlands are located in southwest Spain, on the right bank of the Guadalquivir River, near the Atlantic Ocean coast. The wetlands dry out completely every summer and progressively flood again throughout the fall and winter seasons. Given the flatness of Doñana’s topography, the wind drag action can induce the flooding or emergence of extensive areas, detectable in remote sensing images. Envisat/ASAR scenes acquired before and during strong and persistent wind episodes enabled the spatial delineation of the wind-induced water displacement. A two-dimensional hydrodynamic model of Doñana wetlands was built in 2006 with the aim to predict the effect of proposed hydrologic restoration actions within Doñana’s basin. In this work, on-site wind records and concurrent ASAR scenes are used for the calibration of the wind-drag modeling by assessing different formulations. Results show a good adjustment between the modeled and observed wind drag effect. Displacements of up to 2 km in the wind direction are satisfactorily reproduced by the hydrodynamic model, while including an atmospheric stability parameter led to no significant improvement of the results. Such evidence will contribute to a more accurate simulation of hypothetic or design scenarios, when no information is available for the atmospheric stability assessmen

A SOM-based Chan–Vese model for unsupervised image segmentation

Active Contour Models (ACMs) constitute an efficient energy-based image segmentation framework. They usually deal with the segmentation problem as an optimization problem, formulated in terms of a suitable functional, constructed in such a way that its minimum is achieved in correspondence with a contour that is a close approximation of the actual object boundary. However, for existing ACMs, handling images that contain objects characterized by many different intensities still represents a challenge. In this paper, we propose a novel ACM that combines—in a global and unsupervised way—the advantages of the Self-Organizing Map (SOM) within the level set framework of a state-of-the-art unsupervised global ACM, the Chan–Vese (C–V) model. We term our proposed model SOM-based Chan– Vese (SOMCV) active contourmodel. It works by explicitly integrating the global information coming from the weights (prototypes) of the neurons in a trained SOM to help choosing whether to shrink or expand the current contour during the optimization process, which is performed in an iterative way. The proposed model can handle images that contain objects characterized by complex intensity distributions, and is at the same time robust to the additive noise. Experimental results show the high accuracy of the segmentation results obtained by the SOMCV model on several synthetic and real images, when compared to the Chan–Vese model and other image segmentation models

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

A Fusion of Remotely Sensed Data to Map the Impervious Surfaces of Growing Cities of Punjab, Pakistan

Urban population is expected to exceed 70% of the world’s total by the middle of the 21st century. Thus, growth in number as well as the sizes of the cities are certain in the near future. The urbanization rates will be much higher in the developing countries than the developed. Such phenomena are accompanied by conversion of land cover from its natural use to built up environment to accommodation growing population. Built up surfaces include road networks, buildings, parking lots and pathways. They are permanently impervious and hydrologically active surfaces. Large volume and discharges of runoff characterize impervious surfaces with frequently occurring flash floods in cities. Besides, proliferating impervious surface is responsible for increasing surface temperature due to Urban Heat Island effect and are the major Nonpoint Source pollutants in the receiving water bodies. At the face of climate change, the consequence of urbanization and increasing impervious surface is exacerbating. Therefore, for sustainable development, spatial and temporal expansion information of impervious surfaces is essential to the planners. Thus, the overall objective of this thesis is to map the impervious surfaces and estimate the expansion rates in the growing cities of Punjab, Pakistan in the last four years. In this thesis, combined and individual datasets from Sentinel-1 and Sentinel-2 satellites were used to extract the amounts of impervious surfaces at city scale and to estimate the expansion rates of various cities of Punjab, Pakistan. The study period for the change analysis is from 2015-2021 based on the availability of satellite imagery. The satellite imageries were obtained from the Copernicus Services Data Hub. Information on different land covers in the form of reflectance, backscattering signal, and texture from a wide range of electromagnetic spectrum of light derived from Sentinels were used to map impervious surfaces. The following land covers were defined: barren soil, vegetation, water, and built-up surface. Four classification models were created from Random Forest algorithms and trained with land covers samples from Google Earth high resolution imagery. The 10 cities considered in this study were among the 50 cities extensively studied by the Urban Unit Pakistan covering the dynamics of Punjab in terms of urban extent, population distribution, area, and expansion. They make up the 21st largest cities in the province as well as represent spatial distribution from north to south. They include various climatic conditions ranging from arid in Multan to humid subtropical in Rawalpindi. They also represent different topographies of the cities such as plain and hilly. Validation samples for each land cover were also obtained from high resolution images to assess the classified land cover maps. Apart from validation of classified maps, quantitative comparison of resultant impervious surfaces was also conducted. For the purpose, the study used published datasets from Atlas of Urban Expansion and the Copernicus Land Service. If available, administrative boundaries of the cities were also used to define the urban extent. For other cities, coordinates were manually defined. The combined Sentinel datasets were able to improve the overall accuracy and kappa coefficient of the classified maps by up to 11% and 7% respectively. McNemar test revealed that the models trained with fused data performed better than the models trained with optical alone data for land cover classification. The cities were expanding at rates ranging from 0.5% to 2.5% annually. The highest rate was encountered in Rawalpindi-Islamabad which is also the capital city of Pakistan. At least for one of the study years (2015/6 or 2020/21) the area was being overestimated by the single optical data. For instance, the optical data overestimated the impervious area of Lahore by a factor of 1.12 times while that of Bahawalpur by a factor of 1.2 times. The incorrect original results attributed to misclassification of barren soil as built up. This conclusion emphasized that additional information on backscattering signal and texture derived from radar image aided to reduce the misclassified bare soil pixels into built up. Spectrum plots also showed that sigma db and variance bands from radar image added a distinct feature to the classifier to distinguish built-up surfaces from other non built-up surfaces. The built-up surface had the highest value in backscatter signals and variance texture bands. This study emphasized the usefulness of combining freely available remote sensing datasets for updating the city scale impervious surfaces cover information in developing countries. The contribution includes the assessment of suitability of combined Sentinel datasets to map the impervious surface at city scale. It also evaluates the rate of expansion of the cities. In conclusion, the combined radar and optical data can enhance the accuracy of classified maps for impervious cover mapping with benefits in complex topographies to update impervious surface information in developing countries. The results from this study could be used as inputs in hydrological and runoff models for urban studies. Other useful applications could be service allocation, drainage improvement, location determination for low impact development (LID) structures, stormwater utility fee determinations, flood control, and pollutants removal from runoff