Titlebook: Remote Sensing Big Data; Liping Di,Eugene Yu Book 2023 Springer Nature Switzerland AG 2023 Geospatial big data.Remote sensing.Cyberinfrast

Keshan-disease

Algorithmic Design Considerations of Big Data Analytics,gn to consider data locality and data heterogeneity. Veracity requires algorithm design to consider data provenance and data quality. Value requires algorithm design to consider domain-specific models and knowledge.

拱形大橋

Remote Sensing Big Data Computing,ercomputer, grid computing, and cloud computing. Service-oriented architecture is discussed in detail. Specialized big data processing frameworks are reviewed, including MapReduce/Hadoop, SPARK, and SCALE.

Exposition

Modeling, Prediction, and Decision Making Based on Remote Sensing Big Data,ecasts the phenomena information through time series of data products derived from remote sensing big data, of which predicative methods/algorithms are discussed in Sect. .. The decision support stage is to link the data products with the decision problems to produce and deliver actionable information.

pulmonary

glowing

MERIT

Remote Sensing,imaging sonar. By work mode, there are frame camera, whiskbroom scanner, pushbroom scanner, side-scanning sensor, and conical scanning sensor. Major platforms for remote sensing are also reviewed, including satellites, airborne, in situ, and shipborne.

Indicative

追逐

DEFT

crescendo

Big Data Analytic Platforms,formation, scientific domains, and knowledge and intelligence. Several open-source big data analytic platform software packages are reviewed, including GeoMesa, GeoTrellis, and RasterFrames. Selected remote sensing big data analytic infrastructures are reviewed, including Google Earth Engine, EarthS