The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3/W10
07 Feb 2020
 | 07 Feb 2020


S. Wu, R. C. Qu, C. L. Pan, Z. Y. Bao, and X. J. Feng

Keywords: Geomatics big data, Smart poverty alleviation, Architecture, Poverty alleviation recommendation, Multi-dimensional correlation analysis

Abstract. With the rapid development of geomatics industry, it has accumulated a large amount of data such as digital city and national geoinformation survey, entered the geomatics big data era. At present, there are some researches on the collection and display of poverty alleviation based on geomatics. However, there are relatively few studies on smart poverty alleviation. Many problems need to be solved. It proposes a smart poverty alleviation architecture based on large geomatics data.It realizes the collection and monitor of smart poverty alleviation in administrative regions at all Levels, such as household, village, Township and county.It realizes the visualization of smart poverty alleviation with geomatics big data.It can poverty alleviation recommendation precisely.Aiming at this model,it proposes a precise poverty alleviation recommendation algorithm based on multi-dimensional correlation analysis. It uses Higher Order Singular Value Decomposition (HOSVD) algorithm to mine the relationship between geomatics and poverty alleviation, and recommends poverty alleviation policies. The research has certain practice and test. The architecture can effectively recommend poverty alleviation assistance policies, improve the efficiency of poverty alleviation archives collation, shorten the period of poverty alleviation archives collation, and improve the storage and access methods of poverty alleviation archives. It improves the efficiency of poverty alleviation collection, monitoring and assistance.