Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W2, 137-138, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W2-137-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
05 Jul 2017
PROCESSING BIG REMOTE SENSING DATA FOR FAST FLOOD DETECTION IN A DISTRIBUTED COMPUTING ENVIRONMENT
A. Olasz1, D. Kristóf1, B. Nguyen Thai2, M. Belényesi1, and R. Giachetta2 1Dept. of Geodesy, Remote Sensing and Land Administration, Government Office of the Capital City Budapest, 5.Bosnyák sqr. Budapest, 1149 Hungary
2Dept. of Cartography and Geoinformatics, Eötvös Loránd University (ELTE), 1/A Pázmány Péter sétány, Budapest, 1117 Hungary
Keywords: Distributed Computing, Geospatial Big Data, Cloud Computing, Fast Flood detection, Big Earth Observation Data 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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Citation: Olasz, A., Kristóf, D., Nguyen Thai, B., Belényesi, M., and Giachetta, R.: PROCESSING BIG REMOTE SENSING DATA FOR FAST FLOOD DETECTION IN A DISTRIBUTED COMPUTING ENVIRONMENT, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W2, 137-138, https://doi.org/10.5194/isprs-archives-XLII-4-W2-137-2017, 2017.

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