  The major Sustainable Development Goals (SDG) 2030, set by the United Nations
Development Program (UNDP), include sustainable cities and communities, no
poverty, and reduced inequalities. However, millions of people live in slums or
informal settlements with poor living conditions in many major cities around
the world, especially in less developed countries. To emancipate these
settlements and their inhabitants through government intervention, accurate
data about slum location and extent is required. While ground survey data is
the most reliable, such surveys are costly and time-consuming. An alternative
is remotely sensed data obtained from very high-resolution (VHR) imagery. With
the advancement of new technology, remote sensing based mapping of slums has
emerged as a prominent research area. The parallel rise of Artificial
Intelligence, especially Deep Learning has added a new dimension to this field
as it allows automated analysis of satellite imagery to identify complex
spatial patterns associated with slums. This article offers a detailed review
and meta-analysis of research on slum mapping using remote sensing imagery from
2014 to 2024, with a special focus on deep learning approaches. Our analysis
reveals a trend towards increasingly complex neural network architectures, with
advancements in data preprocessing and model training techniques significantly
enhancing slum identification accuracy. We have attempted to identify key
methodologies that are effective across diverse geographic contexts. While
acknowledging the transformative impact Convolutional Neural Networks (CNNs) in
slum detection, our review underscores the absence of a universally optimal
model, suggesting the need for context-specific adaptations. We also identify
prevailing challenges in this field, such as data limitations and a lack of
model explainability and suggest potential strategies for overcoming these.
