## 1 Introduction
The challenges confronting slum dwellers are manifold, encompassing housing and infrastructure that are vulnerable to flooding and other environmental hazards, resulting in unstable living conditions. Moreover, there is a paucity of clean water and proper and safe sanitation facilities. In addition, there are gaps in health services, limited educational opportunities, and economic disparities. Slum mapping methodologies can be classified into three primary categories: survey-based, participatory, and remote sensing (RS)-based approaches [3]. While both survey-based and participatory methods demand significant human resources for effective implementation, RS-based techniques are less resource intensive in terms of fieldwork; however, they necessitate specialized expertise in remote sensing for accurate image interpretation [4]. The advantages of utilizing RS data include the ability to conduct regular updates and the capacity to gather information from aerial perspectives, which are particularly beneficial for areas that are otherwise difficult to access on the ground [5]. In recent years, high-resolution remote sensing technology has seen widespread application in the field of mapping and analyzing changes in land cover, as evidenced by numerous research studies [6]. This land cover change has been identified as a key indicator in the assessment of remote sensing slum mapping techniques, as the previous researchers have extensively explored its potential [3]. A conventional approach that has been employed is visual image interpretation, wherein experts manually delineate slums. While this method has been shown to produce highly accurate slum maps [7], it is inherently time consuming and its accuracy is dependent on the subjective perspectives of the experts involved. Consequently, disparities in boundary identification may emerge among different experts [8]. In instances where official delineations of slum areas are deemed to be obsolete or incompatible with contemporary realities, visual interpretation is frequently utilized as a means to validate boundaries defined by alternative methods. An alternative to manual delineation is the Automatic Machine Learning (ML) method, which offers a systematic approach to defining slum boundaries using remote sensing imagery (RSI). Within this framework, Object-Based Image Analysis (OBIA) represents a specific subset of ML techniques designed for slum mapping [9]. This process begins with the segmentation of images, wherein adjacent pixels are clustered into 'objects' that correspond to significant features within the imagery, such as buildings and vegetation. Each object is characterized by attributes like size and shape, which are instrumental in identifying slum areas through a predefined set of rules [10; 11]. However, the formulation of these rule sets poses challenges, as the morphological characteristics of slums can vary significantly both between and within urban areas [12]. It is evident that contemporary deep learning (DL) methodologies have become a prevailing trend within the domain of image classification, encompassing applications in the field of slum mapping. These advanced techniques utilize artificial neural networks to automatically learn features from data, with the potential to improve the accuracy and efficiency of slum identification in comparison to traditional methods. The integration of DL with existing ML approaches, such as Random Forest (RF), enables researchers to enhance the classification of image objects in complex urban landscapes. This combination of methodologies has been demonstrated to enhance the accuracy of slum mapping, while also circumventing the limitations frequently associated with rule-based OBIA techniques. Consequently, this approach provides a more robust solution for urban analysis [9; 13]. Remote sensing imagery offers a broad perspective of a region but falls short in conveying intricate details that are essential for a comprehensive understanding of slum environments.