## 1 Introduction  
Climate change is increasingly manifesting in the forms of higher temperatures and more intense storms, leading to a rise in both the frequency and severity of flooding [1]. These climate-induced events underline the imperative need to develop effective flood-resilience measures, with a focus on early detection for managing and mitigating associated risks [2]. Flood management holds a critical role in achieving Sustainable Development Goals (SDGs) related to clean water and sanitation (SDG 6); sustainable cities and communities (SDG 11); and climate action (SDG 13). Recent data from the Centre for Research on the Epidemiology of Disasters revealed that floods accounted for over 47% of weather-related disasters between 1995 and 2015. This alarming statistic underscores the urgency of transitioning to a resilience-based approach to flood risk management [3]. This approach, in line with SDG 6, recognizes that floods can have a direct impact on water quality and sanitation by causing contamination and damaging infrastructure. Effectively managing floods becomes integral to achieving the goal of clean water and sanitation, ensuring that flooding does not jeopardize the well-being and health of affected communities. In recent years, uncontrolled urban expansion and unplanned development have encroached upon floodplains, obstructing natural water flows and escalating flood risks, leading to extensive financial losses. Accurate flood inundation mapping plays a crucial role in guiding urban planning to mitigate these challenges [4]. This aligns directly with SDG 11, emphasizing the need for sustainable and resilient human settlements. Planning developments in a way that prevents the encroachment of settlements onto flood-prone areas is essential for ensuring the safety, resilience, and sustainability of communities and urban environments, which are crucial for achieving SDG 11 and its focus on building resilient and inclusive cities. A common approach to generating flood inundation maps is the pixel-level segmentation of satellite images. Two primary satellite sensors commonly used in generating flood inundation maps are synthetic aperture radar (SAR) and optical sensors. SAR sensors are capable of functioning regardless of the time of day and can penetrate cloud cover, making them ideal for regions prone to climate-induced cloud cover [5]. However, they come with the challenge of speckle noise, which can affect the accuracy of flood inundation mapping models. On the other hand, optical sensors provide a wealth of spectral information, allowing models to distinguish various features, such as vegetation and water. This can enhance the models' ability to delineate floodwaters from other features when trained with optical imagery; however, they are unable to penetrate through cloud. Alternatively, sub-pixel-level processing allows for the analysis of images with finer spatial detail, capturing information beyond the scale of individual pixels. This method involves estimating the fractional cover of specific features, such as water within a single pixel. Sub-pixel processing has proven to be a promising technique, particularly in developing more accurate flood inundation maps [6]. An associated approach, known as super-resolution mapping, further refines this concept. In super-resolution mapping, pixels are deconstructed into sub-pixels, and each sub-pixel is assigned a class label. This technique addresses the challenge of mixed pixels, common in low-resolution images where a single pixel may contain more than one type of land cover [7]. Although this has been shown to provide high levels of accuracy, it has a high computational cost, and can also be sensitive to noise, limiting its practical applicability. The transition from traditional flood modeling techniques to advanced artificial intelligence, particularly machine and deep learning, is driven by the need for more accurate and efficient flood predictions [8]. These advances enable models to make quicker and more precise predictions.