Remote sensing instruments are continuously evolving in terms of spatial, spectral and temporal resolutions and hence provide exponentially increasing amounts of raw data. These volumes increase significantly faster than computing speeds. All these techniques record lots of data, yet in different data models and representations; therefore, resulting datasets require harmonization and integration prior to deriving meaningful information from them. All in all, huge datasets are available but raw data is almost of no value if not processed, semantically enriched and quality checked. The derived information need to be transferred and published to all level of possible users (from decision makers to citizens). Up to now, there are only limited automatic procedures for this; thus, a wealth of information is latent in many datasets. This paper presents the first achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of big geospatial data in the context of flood and waterlogging detection.
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机译：遥感仪器在空间，光谱和时间分辨率方面不断发展，因此提供的原始数据量呈指数增长。这些数量的增长速度明显快于计算速度。所有这些技术记录了大量数据，但是使用不同的数据模型和表示形式。因此，在从数据集中获取有意义的信息之前，需要对它们进行统一和集成。总而言之，可以使用庞大的数据集，但是如果不进行处理，进行语义丰富和质量检查，原始数据几乎毫无价值。需要将派生的信息传输并发布给所有级别的可能用户（从决策者到公民）。到目前为止，只有很少的自动程序可以执行此操作。因此，许多数据集中都蕴藏着丰富的信息。本文介绍了IQmulus EU FP7研究与开发项目在洪水和涝灾检测背景下处理和分析大地理空间数据方面的首项成果。
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