TY  - JOUR
TI  - Remote Sensing Application with Real-Time Big Data Systematic Construction 
AU  - N. Sendhil Kumar
AU  -  R. Sravan Kumar Reddy
AU  -  T. Someswara Reddy
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2018/04/30
PY  - 2018
DO  - https://doi.org/10.32628/IJSRCSEIT
UR  - https://ijsrcseit.com/CSEIT1833589
VL  - 3
IS  - 4
SP  - 1227	
EP  - 1231
AB  - In today's era, there is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a major computational challenges, such as to analyze, aggregate, and store, where data are remotely collected. In the existing system we used a new convolution neural network based multimodal disease risk prediction (CNN-MDRP) algorithm using structured and unstructured data. The projected design contains three main units, like 1) remote sensing huge knowledge acquisition unit (RSDU); 2) processing unit (DPU); and 3) knowledge analysis decision unit (DADU). First, RSDU acquires knowledge from the satellite and sends this data to the base Station, wherever initial process takes place. Second, DPU plays an important role in design for efficient processing of period big data by providing filtration, load equalization, and parallel processing. Third, DADU is that the upper layer unit of the projected design, that is accountable for compilation, storage of the results, and generation of call based on the results received from DPU. The projected design has the capability of dividing, load equalization, and parallel processing of only helpful data. Thus, it results in efficiently analyzing real-time remote sensing huge knowledge victimization earth observatory system. Moreover, the projected design has the potential of storing incoming data to perform offline analysis on largely keep dumps, once needed. Finally, detailed analyses of remotely detected earth observatory huge data for land and ocean space are provided using Hadoop. in addition, various algorithms are proposed for every level of RSDU, DPU, and DADU to find land as well as sea area to elaborate the operating of an architecture.