Abstract
Geospatial data analysis can be improved by using data-driven algorithms and techniques from the machine learning field. The aim of our research is to discover interrelationships among topographical data to support the decision-making process. In this paper, we extracted topographical geospatial data from digital elevation model (DEM) raster images, and we discovered hidden patterns among this data based on the K-means clustering algorithm, to uncover relationships and find clusters of elevation values for the area of Jordan. We introduce a method for querying and clustering geospatial data and we built an interactive map accordingly. The method discovers hidden patterns and uncovers relationships in given large datasets. We demonstrate the applicability of the method using the Jordan map and we report on geospatial data analysis and retrieval improvements. The results show that the optimal decision is in favor of four clusters (classes). The first class includes the high elevation values, the second class includes the very low elevation values, the third class includes the medium-high elevation values, and the fourth class includes the very high elevation values.
Highlights
A digital elevation model (DEM) is a digital representation of the bare ground topographic surface of the earth excluding trees, buildings, and any other surface objects [1]
We argue that the K-means cluster is a perfect fit to solve geospatial data-related problems and improve DEM data processing and analysis, intending to support decision-making
We presented a method for topographical data clustering in Jordan, based on elevation similarities using the K-means algorithm
Summary
A digital elevation model (DEM) is a digital representation of the bare ground (bare earth) topographic surface of the earth excluding trees, buildings, and any other surface objects [1]. DEM is a 3D representation of the terrain that shows elevation data as computer graphics. DEMs are a critical component of the representation of the digital earth and are frequently used in GIS, as they are the most common basis for digitally-produced relief maps [3]. They can be represented either as a raster or as vector-based triangular irregular network (TIN) [2]. While the term can be used for any representation of terrain as GIS data, it is generally restricted to the use of a raster grid of elevation values [4]. DEMs are commonly built using remote sensing techniques, but they may be built from land surveying [5]
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