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In the intricate landscape of mapping urban slum dynamics, the significance of robust and efficient techniques is often underestimated and remains absent in many studies. This not only hampers the comprehensiveness of research but also undermines potential solutions that could be pivotal for addressing the complex challenges faced by these settlements. With this ethos in mind, we prioritize efficient methods to detect the complex urban morphologies of slum settlements. Leveraging transfer learning with minimal samples and estimating the probability of predictions for slum settlements, we uncover previously obscured patterns in urban structures. By using Monte Carlo dropout, we not only enhance classification performance in noisy datasets and ambiguous feature spaces but also gauge the uncertainty of our predictions. This offers deeper insights into the model's confidence in distinguishing slums, especially …