Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach

• URL: http://arxiv.org/abs/2204.04419v1
• Date: Sat, 9 Apr 2022 08:02:32 GMT
• Title: Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach
• Authors: M. Fasi ur Rehman, Izza Ali, Waqas Sultani, Mohsen Ali
• Abstract summary: One billion people worldwide are estimated to be living in slums. Small, scattered and temporary slums make data collection and labeling tedious and time-consuming. We present a semi-supervised deep learning segmentation-based approach to detect temporary slums.
• Score: 5.830619388189557
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One billion people worldwide are estimated to be living in slums, and documenting and analyzing these regions is a challenging task. As compared to regular slums; the small, scattered and temporary nature of temporary slums makes data collection and labeling tedious and time-consuming. To tackle this challenging problem of temporary slums detection, we present a semi-supervised deep learning segmentation-based approach; with the strategy to detect initial seed images in the zero-labeled data settings. A small set of seed samples (32 in our case) are automatically discovered by analyzing the temporal changes, which are manually labeled to train a segmentation and representation learning module. The segmentation module gathers high dimensional image representations, and the representation learning module transforms image representations into embedding vectors. After that, a scoring module uses the embedding vectors to sample images from a large pool of unlabeled images and generates pseudo-labels for the sampled images. These sampled images with their pseudo-labels are added to the training set to update the segmentation and representation learning modules iteratively. To analyze the effectiveness of our technique, we construct a large geographically marked dataset of temporary slums. This dataset constitutes more than 200 potential temporary slum locations (2.28 square kilometers) found by sieving sixty-eight thousand images from 12 metropolitan cities of Pakistan covering 8000 square kilometers. Furthermore, our proposed method outperforms several competitive semi-supervised semantic segmentation baselines on a similar setting. The code and the dataset will be made publicly available.

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