Transfer learning approach to map urban slums using high and medium resolution satellite imagery

Abstract Slums provide cheaper workforce and informal services which contribute substantially towards GDP. However, such areas, due to the high population density, sub-standard housing and lack of essential services are urban risks. The socio-physical development of such settlements has often been neglected due to poor laws and provisions in urban management and policies. One of the primary reasons for negligence has been the unavailability of slum maps to study the evolution of slums and to actively manage and contain them. Various remote sensing techniques have been utilized to answer the problem but have not produced universal solutions. In recent years, Deep Learning (DL) techniques with remote sensing have been found beneficial in comprehending the underlying structure of physical features present in the satellite imageries. This study deals with one of the Deep Learning techniques which use pre-trained convolutional networks for slum detection in Very High Resolution (VHR) and Medium Resolution (MR) satellite imagery. We created a training dataset which comprises of four classes including slums, built, green and water. We further trained the model to detect these classes in the entire city. Classification performance was evaluated for Very high and Medium Resolution imagery with the help of manually delineated slum boundaries gathered from urban local authorities of Mumbai. The Overall accuracy of 94.2 and 90.2 and kappa of 0.70 and 0.55 is obtained from VHR and MR imagery respectively. We provide a comprehensive technique for the detection of informal settlements which can be tailored and applied to any city to detect various landforms.