A Scalable Complex Pattern Mining Framework for Global Settlement Mapping

Human settlements manifest as complex spatial patterns in very high-resolution (VHR) satellite remote sensing images. Widely used pixel and object-based methods are incapable of capturing these complex patterns. Recently developed multiple instance learning algorithms showed to be very effective in mapping different types of human settlements. However, multiple instance learning approaches are computationally expensive and do not scale for global scale problems using big VHR imagery data. In this paper, we extend the Gaussian Multiple Instance (GMIL) learning by simplifying the model assumptions. Experimental evaluation shows that this method is computationally more efficient while maintaining similar accuracy as the GMIL algorithm.