Work

Enhancing Slum Identification and Definition Through Deep Learning and Spatial Data Integration

Year: 2025

Type: conference-paper

Abstract: This research investigates key slum characteristics to enhance detection accuracy using deep learning models. Four databases were reviewed with BOOLEAN operations and keyword combinations, following PRISMA guidelines. The data was organized and synthesized in Excel for clarity. The results show that standardized slum definitions improve model performance. Additionally, integrating spatial datasets and remote sensing techniques refines detection accuracy. This work demonstrates how deep learning, combined with GIS tools and census data, can improve slum identification and boundary delineation. The findings highlight the potential of these tools to support more precise and effective slum detection strategies.

Source: Loyola eCommons (Loyola University of Chicago)

Author: Coolen, Keiron

Language: English

Topic: Urban and Rural Development Challenges

Subfield: Urban Studies

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Reduced inequalities

Open Access status: green

OpenAlex ID: W7009600255

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