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Advances of Four Machine Learning Methods for Spatial Data Handling: a Review

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Abstract

Most machine learning tasks can be categorized into classification or regression problems. Regression and classification models are normally used to extract useful geographic information from observed or measured spatial data, such as land cover classification, spatial interpolation, and quantitative parameter retrieval. This paper reviews the progress of four advanced machine learning methods for spatial data handling, namely, support vector machine (SVM)-based kernel learning, semi-supervised and active learning, ensemble learning, and deep learning. These four machine learning modes are representative because they improve learning performances from different views, for example, feature space transform and decision function (SVM), optimized uses of samples (semi-supervised and active learning), and enhanced learning models and capabilities (ensemble learning and deep learning). For spatial data handling via machine learning that can be improved by the four machine learning models, three key elements are learning algorithms, training samples, and input features. To apply machine learning methods to spatial data handling successfully, a four-level strategy is suggested: experimenting and evaluating the applicability, extending the algorithms by embedding spatial properties, optimizing the parameters for better performance, and enhancing the algorithm by multiple means. Firstly, the advances of SVM are reviewed to demonstrate the merits of novel machine learning methods for spatial data, running the line from direct use and comparison with traditional classifiers, and then targeted improvements to address multiple class problems, to optimize parameters of SVM, and to use spatial and spectral features. To overcome the limits of small-size training samples, semi-supervised learning and active learning methods are then utilized to deal with insufficient labeled samples, showing the potential of learning from small-size training samples. Furthermore, considering the poor generalization capacity and instability of machine learning algorithms, ensemble learning is introduced to integrate the advantages of multiple learners and to enhance the generalization capacity. The typical research lines, including the combination of multiple classifiers, advanced ensemble classifiers, and spatial interpolation, are presented. Finally, deep learning, one of the most popular branches of machine learning, is reviewed with specific examples for scene classification and urban structural type recognition from high-resolution remote sensing images. By this review, it can be concluded that machine learning methods are very effective for spatial data handling and have wide application potential in the big data era.

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Funding

This study is funded by the National Natural Science Foundation of China (Grant No. 41631176).

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Authors and Affiliations

  1. School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China

    Peijun Du & Xuyu Bai

  2. Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources of China, Nanjing, 210023, China

    Peijun Du & Xuyu Bai

  3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China

    Peijun Du & Xuyu Bai

  4. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, 200241, China

    Kun Tan

  5. School of Earth Sciences and Engineering, Hohai University, Nanjing, 211100, China

    Zhaohui Xue & Hongjun Su

  6. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

    Alim Samat

  7. RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan

    Junshi Xia

  8. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, 221116, China

    Erzhu Li & Wei Liu

Authors
  1. Peijun Du
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  2. Xuyu Bai
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  3. Kun Tan
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  4. Zhaohui Xue
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  5. Alim Samat
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  6. Junshi Xia
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  7. Erzhu Li
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  8. Hongjun Su
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  9. Wei Liu
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Correspondence to Peijun Du.

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Du, P., Bai, X., Tan, K. et al. Advances of Four Machine Learning Methods for Spatial Data Handling: a Review. J geovis spat anal 4, 13 (2020). https://doi.org/10.1007/s41651-020-00048-5

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  • DOI: https://doi.org/10.1007/s41651-020-00048-5

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