# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 1 Introduction
Transformers have also been successfully applied for semantic segmentation in remote sensing images. A novel transformer-based scheme employing the Swin Transformer as the backbone to better capture context information and a densely connected feature aggregation module (DCFAM) serving as a novel decoder to restore resolution and generate accurate segmentation maps proved to be effective in the ISPRS Vaihingen and Potsdam datasets [21]. An improved transformer-based multiclass flood detection model capable of predicting flood events while distinguishing between roads and buildings was introduced, which, with an additional novel loss function and a road noise removal algorithm, achieved superior performance, particularly in road evaluation metrics such as APLS [17]. Finally, the Bitemporal image Transformer (BiT) model scored highest in a change detection approach that better captures the changed region [7]. Dilated or atrous convolutions, which increase the network's receptive field and reduce the number of trained parameters needed [30], are utilized in an effort to speed up search and rescue operations after natural disasters, such as floods, high tides, and tsunamis. FASegNet, a novel CNN-based model featuring dilated convolutions, was specifically designed for flood and tsunami area segmentation. FASegNet utilizes encoder and decoder networks with an encoder-decoder-residual (EDR) block to effectively extract local and contextual information. An encoder-decoder high-accuracy activation cropping (EHAAC) module minimizes information loss at the bottleneck, and skip connections transfer information between the encoder and decoder networks, outperforming other segmentation models [19]. A novel weak training data generation strategy and an end-to-end weakly supervised semantic segmentation (WSSS) method, called TFCSD, challenges urban flood mapping [9]. By decoupling the acquisition of positive and negative samples, the weak label generation strategy significantly reduces the burden of data labeling, enabling quick flood mapping in emergencies. Additionally, the proposed TFCSD method improves edge delineation accuracy and algorithm stability compared to other methods, especially in emergency scenarios where pre-disaster river data are accessible, or when using the SAM ([31]) assisted interactive labeling method if such data are unavailable. Satellites such as Sentinel-1 and Sentinel-2 play a key role in flood mapping due to their rapid data acquisition capabilities. Their effectiveness in mapping floods across Europe was evaluated in a study in which the results indicate that observation capabilities vary based on the size of the catchment area, and suggest that employing multiple satellite constellations significantly increases flood mapping coverage [32]. The urgent need for real-time flood management systems by developing an automated imaging system using unmanned aerial vehicles (UAVs) to detect inundated areas promptly is addressed, so that emergency relief efforts will not be hindered by current satellite-based imaging systems, which suffer from low accuracy and delayed response. By employing the Haar cascade classifier and DLalgorithms, a hybrid flood detection model combining landmark-based feature selection with a CNN demonstrated improved performance over traditional classifiers [16]. Specially designed datasets have been introduced to address the lack of high-resolution (HR) imagery relevant to disaster scenarios. In [18], FloodNet, a high resolution (HR) UAV imagery dataset, capturing post-flood damage, aims to detect flooded roads and buildings and distinguish between natural and flooded water. Baseline methods for image classification, semantic segmentation, and visual question are evaluated, highlighting its significance for analyzing disaster impacts with various DL algorithms, such as XceptionNet and ENet.