Abstract
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.
Highlights
Geospatial Big Data has an established and well-known definition (Lee and Kang 2015, Li et al 2016, Olasz A. and Nguyen Thai B and Kristóf D. 2016)
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
IQmulus developed functional and domain processing services in order to maximize the use of geospatial big data and provide support for analysing quickly changing environmental conditions
Summary
Geospatial Big Data has an established and well-known definition (Lee and Kang 2015, Li et al 2016, Olasz A. and Nguyen Thai B and Kristóf D. 2016). Geospatial Big Data has an established and well-known definition Huge datasets are available but up to now there are only limited automatic procedures for processing; as a huge amount of data remains unprocessed, a wealth of information is latent in many datasets. A number of distributed data processing solutions exist, but have primarily been focused on processing simple structured documents rather than complex geospatial data. This paper presents a multi-sensor remote sensing processing solution within the IQmulus Project to realize fast flood and waterlogging detection in a cloud environment
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