Using big geospatial data for fast flood detection: Developments from the IQmulus project

Kristóf, Dániel; Giachetta, Roberto; Olasz, Angéla; Belenyesi, Marta; Thai, Binh; Harsanyi, Melinda

doi:10.1109/igarss.2015.7325895

Cited by 3 publications

(1 citation statement)

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“…An end-to-end involvement of users was ensured through the implementation of three concrete "test beds" (Maritime Spatial Planning & Land Applications for Rapid Response and Territorial Management) to show the benefits of the approach (Kristóf et al 2014). In this study we are focusing on the Land Application for Rapid Response test bed, which includes the showcase of Detection and characterization of flood and waterlogging.…”

Section: The Iqmulus Projectmentioning

confidence: 99%

Processing Big Remote Sensing Data for Fast Flood Detection in a Distributed Computing Environment

Olasz¹,

Kristóf²,

Thai

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Earth observation (EO) missions of the space agencies and space industry (ESA, NASA, national and commercial companies) are evolving as never before. These missions aim to develop and launch next-generation series of satellites and sensors and often provide huge amounts of data, even free of charge, to enable novel monitoring services. The wide geospatial sector is targeted to handle new challenges to store, process and visualize these geospatial data, reaching the level of Big Data by their volume, variety, velocity, along with the need of multi-source spatio-temporal geospatial data processing. Handling and analysis of remote sensing data has always been a cumbersome task due to the ever-increasing size and frequency of collected information. This paper presents the achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of geospatial big data in the context of flood and waterlogging detection.

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Section: The Iqmulus Projectmentioning

confidence: 99%