Detecting slum from satellite images using convolution network: A case study in the city of Kenitra

Abstract

This article provides a comprehensive review of advancements in detecting and locating slum images, particularly focusing on Kenitra, Morocco. It explores the socioeconomic impacts of informal housing, such as limited access to basic services and increased vulnerability. The article also discusses urban planning challenges, including insufficient infrastructure and the lack of formal recognition for informal settlements. Technological solutions, such as remote sensing and artificial intelligence, are emphasized as key tools for accurate mapping and monitoring. Furthermore, it highlights the importance of participatory urban planning in involving local communities in decision-making. By integrating these approaches, the review advocates for data-driven solutions to address housing inequities. Ultimately, the goal is to promote sustainable urban development and improve living conditions for marginalized populations.