AUTOMATIC CAL/VAL METHODS LOWERING PRODUCTION EFFORT OF UPDATING LARGE CORE SPATIAL DATA
Keywords: Supervised Classification, Data-Base Driven, Autocalibration, Type-classes, Automatic change detection, Ad-hoc type-class identification, Automatic Updating, Big core spatial Data Bases, Munchhausen method
Abstract. There are basically two levels of calibrations and validation of digitally acquired spectral and other information via sensors carried on space-borne or airborne platforms. The basic level is carried out by the data producers executed by comparison made of results taken over test fields for example. The second level, more a part of a supervised classification effort are carried by the data users and value added spatial information users or providers to edge users. The latter is quite typical for supervised classification protocols. This is either for establishing libraries of spectral signatures for each relevant class-type or for ad-hoc classification where no previous information or specific knowledge wee kept. Such methods indicate and support even strongly the need of the basic Cal/Val step of the sensors made by the original data providers. The paper is reviewing the method of database-driven concept that allows for automatic recognition of detected features within the digital spatial 2-D (yet) realm to its identification within the digital 2.5D spatial vector information within existing large Big-data national core spatial data bases to be updated. These Large data bases are Big enough to operate the resourceful Munchhausen method of self-pulling information out of the huge abandon of data resources.