Temporary Slum

Temporary slums, characterized by their small size, scattered nature, and impermanence, pose significant challenges for accurate mapping and analysis, hindering effective urban planning and poverty alleviation efforts. Current research focuses on leveraging deep learning, particularly convolutional neural networks and graph neural networks, to analyze high-resolution satellite imagery and automatically generate road plans for these settlements, improving accessibility and resource allocation. These advancements, often employing semi-supervised learning techniques to address data scarcity, are crucial for generating reliable data to inform policy and interventions aimed at improving living conditions in these vulnerable communities.