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

by   M. Fasi ur Rehman, et al.

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.


page 1

page 2

page 3

page 5


Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank

This work presents a novel approach for semi-supervised semantic segment...

Semi-supervised Semantic Segmentation with Error Localization Network

This paper studies semi-supervised learning of semantic segmentation, wh...

Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images

Remote sensing data is crucial for applications ranging from monitoring ...

Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

Learning meaningful visual representations in an embedding space can fac...

FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation

Supervised deep learning methods for semantic medical image segmentation...

Semi-Supervised Contrastive Learning for Remote Sensing: Identifying Ancient Urbanization in the South Central Andes

The detection of ancient settlements is a key focus in landscape archaeo...

Holistic Semi-Supervised Approaches for EEG Representation Learning

Recently, supervised methods, which often require substantial amounts of...

Please sign up or login with your details

Forgot password? Click here to reset