## 1 Introduction
It lacks the capacity to reveal specific characteristics such as the materials used in the wall construction, the number of stories in buildings, or the existence of open drainage systems within the community. To acquire this nuanced information, it is essential to conduct ground-level investigations through methods such as field surveys, interviews, focus groups, or by utilizing street view images (SVIs), which capture photographs from the ground perspective. Despite their potential, the application of SVIs in slum identification remains limited, as noted by [14]. Furthermore, the inherent limitations of SVIs must be acknowledged, as they primarily provide localized data or insights along well-traveled streets, thereby failing to generate comprehensive spatial representations of slum areas. For instance, in scenarios where access is restricted to pedestrian pathways, relying solely on SVIs becomes impractical, given that these images are typically captured from vehicles navigating the area. This constraint underscores the necessity for a multifaceted approach to data collection that encompasses various methodologies to accurately map and understand the complexities of slum regions accurately. Meanwhile, Bandung City, characterized by a confluence of population pressure, economic inequality, inadequate governance of land and housing legality, and its basin geography, harbors a high degree of vulnerability to annual encroachment of slum areas. Bandung City Government has hitherto determined slum areas on a quinquennial basis through the utilization of a gradual field survey method. Nevertheless, given the potential for slum areas to expand on an annual basis, there is a need for more inclusive urban governance. Consequently, this research recommends the periodic and inclusive compilation of spatial data inventories to facilitate the effective targeting of slum upgrading initiatives. This inventory can be conducted using an integrated and regular method with a multi-resolution texture analysis method. This method has been developed to identify slums in remote sensing images [15]. Furthermore, the integration of remote sensing networks and street view imagery (SVI) for slum mapping in Jakarta City has been enhanced by employing deep learning networks, including the VGG method [16]. VGG is a convolutional neural network (CNN) that has become synonymous with its deep architecture, which consists of multiple layers. The efficacy of the system is particularly pronounced in image classification tasks, a phenomenon that can be attributed to its capacity to capture intricate features through the utilization of multiple convolutional layers [16; 17]. The efficacy of remote sensing and the incorporation of street view imagery as a tool for mapping slums in Bandung City will be assessed through a comparative analysis with field survey results in several areas of the city in 2024. The conditions described above were the catalyst for the author's research on the spatial mapping inventory of potential slum areas in Bandung City with remote sensing and street view imagery and a comparative analysis of the effectiveness of this method as opposed to field surveys.