The characteristics of spatially explicit data are often inadequately handled in machine learning for spatial domains of application. At the same time, resources that can identify these properties and explore their impacts and how machine learning applications handle them are lagging behind. In this paper, we seek to identify and discuss the spatial properties of data that influence the performance of machine learning. We address existing research efforts and challenges in three main areas of machine learning: data analysis, deep learning and statistical inference. We will also discuss the existing end-to-end systems and highlight unresolved issues and challenges for future research in this area.

Machine Learning, Machine Learning Algorithms, Spatial data, Geospatial Data, Spatial observation matrix, Classification.