The LuFI-RiverSnap dataset includes close-range river scene images obtained from various devices, such as UAVs, surveillance cameras, smartphones, and handheld cameras, with sizes up to 4624 × 3468 pixels. Several social media images, which are typically volunteered geographic information (VGI), have also been incorporated into the dataset to create more diverse river landscapes from various locations and sources. 


Please see the following links: 




We conducted the tests using the **GitLab repository** with Segment Anything Model (SAM) model: https://github.com/ArminMoghimi/RiverSnap


The images mainly include river scenes from several cities in Germany (Hannover, Hamburg, Bremen, Berlin,

and Dresden), Italy (Venice), Iran (Ahvaz), the USA, and Australia.


To further enhance the dataset’s diversity and accuracy, a small subset of images of [**Elbersdorf/Wesenitz**](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN...), [**RIWA.v1**](https://www.kaggle.com/datasets/franzwagner/river-water-segmentation-dat...), and [**Kaggle WaterNet/Water Segmentation Dataset**](https://www.kaggle.com/datasets/gvclsu/water-segmentation-dataset) has been added. 



This comprehensive dataset includes 1092 images, all accurately annotated, establishing it as a valuable resource for advancing research and development in river scene analysis and segmentation. 



The dataset comprises challenging cases for water segmentation, such as rivers with significant reflection, shadows, various colors, and flooded areas. 



If you use this dataset, please cite as:


>A. Moghimi, M. Welzel, T. Celik, and T. Schlurmann, "A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery," in IEEE Access, 

doi: [https://doi.org/10.1109/ACCESS.2024.3385425](https://doi.org/10.1109/ACCESS.2024.3385425)




As you know, other researchers, such as Xabier Blanch, Franz Wagner, and Professor Anette Eltner from TU Dresden, have already provided very perfect water segmentation datasets. We are not the first; please consider the following links for other benchmark datasets. 


[**Elbersdorf/Wesenitz**](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN...), [**RIWA**](https://www.kaggle.com/datasets/franzwagner/river-water-segmentation-dat...), and [**Kaggle WaterNet/Water Segmentation Dataset**](https://www.kaggle.com/datasets/gvclsu/water-segmentation-dataset




- Armin Moghimi





The Paper for some experiments on the dataset.

Data Descriptor Article DOI: