Figure 1: Graphical abstract of our proposed unsupervised approach flood detection [1].

Figure 2: Graphical abstract of our proposed unsupervised Deep Learning approach [2] for flood detection.

Figure 3:Graphical abstract of our proposed unsupervised Deep Learning approach [3].

In [1] we propose a novel unsupervised semantic segmentation method for fast  and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles  (UAVs). To our knowledge so far, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-desaster images. 

In [2], we present a novel unsupervised Deep Learning method for flood segmentation in Unmanned Aerial Vehicle imagery. This method utilizes automatically generated labels as masks for the training process, eliminating the need for actual ground truth data. 

In [3], we present a novel methodology for generating and filtering synthetic Unmanned Aerial Vehicle (UAV) flood imagery to enhance the generalization capabilities of segmentation models. 

In [4], we present AIFloodSense [1] (a Global Aerial Imagery Dataset for Semantic Segmentation and Understanding of Flooded Environments) that provides worldwide coverage and more recent aerial imagery than prior benchmarks.  AIFloodSense  is an aerial imagery dataset comprising 470 high-resolution images from 230 distinct flood events across 64 countries and six continents.