Figure 1:Graphical abstract of our proposed unsupervised approach.

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.

Experiments - Downloads 

The code and the results of the proposed method will be available after the paper publication.

Flood Area Segmentation Dataset (290 images): https://www.kaggle.com/datasets/faizalkarim/flood-area-segmentation 

Flood Semantic Segmentation Dataset  (663 images): https://www.kaggle.com/datasets/lihuayang111265/flood-semantic-segmentation-dataset 

Related Publications

[1] Georgios Simantiris and Costas Panagiotakis, Unsupervised Color Based Flood Segmentation in UAV imagery, submitted to Remote Sensisng, 2024 (under review).