Earth observation and case-based systems for flood risk management

Henry, Jean-Baptiste; Fellah, K.; Clandillon, Stephen; Allenbach, Bernard; Fraipont, P. De

doi:10.1109/igarss.2002.1026160

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this publication. The act of merging many models into a single comprehensive logical model is referred to as "model fusion [9].…”

Section: Review Of Literaturementioning

confidence: 99%

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

William,

Oyebode,

Sharma

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.

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Section: Review Of Literaturementioning

confidence: 99%

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

William,

Oyebode,

Sharma

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…In general, one obtains less than 10 categories for low-resolution images and more than 30 categories for highresolution images, depending on the properties of the categories selected (i.e., manually extracted) by a user, while the number of categories that were identified in rapid mapping applications after a disaster is typically limited to 2 to 7 [13], [14]. In summary, the number of identified and annotated remote sensing categories is very limited compared with multimedia.…”

Section: Semantic Annotation For Multimedia and Remote Sensing Immentioning

confidence: 99%

Information Content of Very-High-Resolution SAR Images: Semantics, Geospatial Context, and Ontologies

Dumitru

Cui

Schwarz

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Currently, the amount of collected Earth Observation (EO) data is increasing considerably with a rate of several Terabytes of data per day. As a consequence of this increasing data volume, new concepts for exploration and information retrieval are urgently needed. To this end, we propose to explore satellite image data via an image information mining (IIM) approach in which the main steps are feature extraction, classification, semantic annotation, and interactive query processing. This leads to a new process chain and a robust taxonomy for the retrieved categories capitalizing on human interaction and judgment. We concentrated on land cover categories that can be retrieved from high-resolution synthetic aperture radar (SAR) images of the spaceborne TerraSAR-X instrument, where we annotated different urban areas all over the world and defined a taxonomy element for each prevailing surface cover category. The annotation resulted from a test dataset comprising more than 100 scenes covering diverse areas of Africa, Asia, Europe, the Middle East, and North and South America. The scenes were grouped into several collections with similar source areas and each collection was processed separately in order to discern regional characteristics. In the first processing step, each scene was tiled into patches. Then the features were extracted from each patch by a Gabor filter bank and a support vector machine with relevance feedback classifying the feature sets into user-oriented land cover categories. Finally, the categories were semantically annotated using Google Earth for ground truthing. The annotation followed a multilevel approach that allowed the fusion of information being visible on different resolution levels. The novelty of this paper lies in the fact that a semantic annotation was performed with a large number of high-resolution radar images that allowed the definition of more than 850 surface cover categories. This opens the way toward an automated identification and classification of urban areas, infrastructure (e.g., airports), geographic objects (e.g., mountains), industrial installations, military compounds, vegetation, and agriculture. Applications that may result from this work can be a semantic catalog of urban images to be used in crisis situations or after a disaster. In addition, the proposed taxonomies can become a basis for building a semantic catalog of satellite images. Finally, we defined four powerful types of high-level queries. Querying on such high levels provides new opportunities for users to search an image database for specific parameters or semantic relationships.

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“…The result is an increase of floods that strike countries with little capacity to cope with new exceptional destructive events. Flood events have a huge impact on human beings, settlements and ways of life [Henry et al, 2002]. The humanitarian and the United Nations system are therefore called to intensify their efforts to improve their capacity to provide support to the countries in need and to be better prepared to intervene.…”

Section: Introduzionementioning

confidence: 99%

Early impact procedures for flood events February 2007 Mozambique flood

Ajmar¹,

Boccardo²,

Disabato³

et al. 2008

ItJRS

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Satellite images and GIS procedures are key elements for emergency management, especially in case of events hitting developing countries, more vulnerable to calamities and less prepared to face them. This article aims to show the procedure applied for the production of a cartography of flooded areas during the early impact phase; these activities are developed within ITHACA, centre of excellence, in charge of giving technological support to the WFP (World Food Programme), the biggest agency of the UN. The flood in Mozambique, occurred in January 2007, is illustrated as an example of events management.

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Earth observation and case-based systems for flood risk management

Cited by 5 publications

References 11 publications

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Information Content of Very-High-Resolution SAR Images: Semantics, Geospatial Context, and Ontologies

Early impact procedures for flood events February 2007 Mozambique flood

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