A Methodology and a Tool to Prepare Agro-Meteorological Maps as a Source of Big Data

The volumetric aspect of big data is increasing because of introduction of new methods to generate-capture data and the diverse needs of that data. Agro-meteorological maps (e.g. Digital maps, printed maps, scanned maps etc.) and satellite imagery are also sources of big data in terms of volume, variety in format, scale, representation, then presence of noise resulting in veracity and the velocity in terms of rate of availability of satellite imagery. One main problem with these domain specific agro-meteorological maps is presence of veracity in terms of noise (i.e. Irrelevant data in maps) such as, city names, symbols, administrative boundaries etc. If we can successfully remove noise from these maps and replace with right data, we can convert these maps into rich sources of digital data resulting in new opportunities in traditional agricultural sector, in policy making and decision support. In this paper we present an approach and its implementation that can be used to minimize the veracity in maps by cleaning the noise from maps and replacing the noise with right data. Once maps are cleaned, right data can be extracted from them. This data can be used by different big data applications and knowledge based systems in decision and policy making in different sectors.