Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery

In this paper, we present a computationally efficient algorithm based on multiple instance learning for mapping informal settlements (slums) using very high-resolution remote sensing imagery. From remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other urban structures like industrial, commercial, and formal residential settlements. However, regular pattern recognition and machine learning methods, which are predominantly single-instance or per-pixel classifiers, often fail to accurately map the informal settlements as they do not capture the complex spatial patterns. To overcome these limitations we employed a multiple instance based machine learning approach, where groups of contiguous pixels (image patches) are modeled as generated by a Gaussian distribution. We have conducted several experiments on very high-resolution satellite imagery, representing four unique geographic regions across the world. Our method showed consistent improvement in accurately identifying informal settlements.