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A Machine Learning-Based Approach for Spatial Estimation Using the Spatial Features of Coordinate Information

Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon 34132, Korea
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ISPRS Int. J. Geo-Inf. 2020, 9(10), 587; https://doi.org/10.3390/ijgi9100587 (registering DOI)
Received: 31 August 2020 / Revised: 25 September 2020 / Accepted: 4 October 2020 / Published: 6 October 2020
With the development of machine learning technology, research cases for spatial estimation through machine learning approach (MLA) in addition to the traditional geostatistical techniques are increasing. MLA has the advantage that spatial estimation is possible without stationary hypotheses of data, but it is possible for the prediction results to ignore spatial autocorrelation. In recent studies, it was considered by using a distance matrix instead of raw coordinates. Although, the performance of spatial estimation could be improved through this approach, the computational complexity of MLA increased rapidly as the number of sample points increased. In this study, we developed a method to reduce the computational complexity of MLA while considering spatial autocorrelation. Principal component analysis is applied to it for extracting spatial features and reducing dimension of inputs. To verify the proposed approach, indicator Kriging was used as a benchmark model, and each performance of MLA was compared when using raw coordinates, distance vector, and spatial features extracted from distance vector as inputs. The proposed approach improved the performance compared to previous MLA and showed similar performance compared with Kriging. We confirmed that extracted features have characteristics of rigid classification in spatial estimation; on this basis, we conclude that the model could improve performance. View Full-Text
Keywords: machine learning; random forest; Kriging; spatial estimation; spatial feature; principal component analysis machine learning; random forest; Kriging; spatial estimation; spatial feature; principal component analysis
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Ahn, S.; Ryu, D.-W.; Lee, S. A Machine Learning-Based Approach for Spatial Estimation Using the Spatial Features of Coordinate Information. ISPRS Int. J. Geo-Inf. 2020, 9, 587.

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