Scalable Multi-Instance Learning Approach for Mapping the Slums of the World

Abstract

Remote sensing imagery is widely used in mapping thematic classes, such as, forests, crops, forests and other natural and man-made objects on the Earth. With the availability of very high-resolution satellite imagery, it is now possible to identify complex patterns such as formal and informal (slums) settlements. However, predominantly used single-instance learning algorithms that are widely used in thematic classification are not sufficient for recognizing complex settlement patterns. On the other hand, newer multi-instance learning schemes are useful in recognizing complex structures in images, but they are computationally expensive. In this paper, we present an adaptation of a multi-instance learning algorithm for informal settlement classification and its efficient implementation on shared memory architectures. Experimental evaluation shows that this approach is scalable and as well as accurate than commonly used single-instance learning algorithms.