LuFI-RiverSnap (River Water segmentation)

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: https://doi.org/10.1109/ACCESS.2024.3385425 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/ONOZRW), [**RIWA.v1**](https://www.kaggle.com/datasets/franzwagner/river-water-segmentation-dataset), 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. #CitationIf 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) #Acknowledgement: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/ONOZRW), [**RIWA**](https://www.kaggle.com/datasets/franzwagner/river-water-segmentation-dataset), and [**Kaggle WaterNet/Water Segmentation Dataset**](https://www.kaggle.com/datasets/gvclsu/water-segmentation-dataset) #Contact:- Armin [e-mail] [e-mail]

LuFI-RiverSnap数据集包含通过多种设备采集的近距离河道场景图像，采集设备包括无人机（UAV）、监控摄像头、智能手机及手持相机，图像分辨率最高可达4624×3468像素。本数据集还纳入了多幅典型属于志愿地理信息（Volunteered Geographic Information, VGI）的社交媒体图像，以丰富不同采集地点与来源的河道景观多样性。详见以下文献：https://doi.org/10.1109/ACCESS.2024.3385425。本研究使用**GitLab代码仓库**结合分割一切模型（Segment Anything Model, SAM）开展测试，相关仓库地址为：https://github.com/ArminMoghimi/RiverSnap。这些图像主要采集自德国汉诺威、汉堡、不来梅、柏林、德累斯顿，意大利威尼斯，伊朗阿瓦士，美国及澳大利亚的多处河道场景。为进一步提升数据集的多样性与准确性，我们还加入了来自[**Elbersdorf/Wesenitz**](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ONOZRW)、[**RIWA.v1**](https://www.kaggle.com/datasets/franzwagner/river-water-segmentation-dataset)以及[**Kaggle WaterNet/Water Segmentation Dataset**](https://www.kaggle.com/datasets/gvclsu/water-segmentation-dataset)的少量图像子集。本完整数据集共包含1092幅经精准标注的图像，可作为推动河道场景分析与分割领域研究及发展的宝贵资源。该数据集涵盖了水体分割任务中的诸多挑战性场景，例如存在明显反光、阴影、色彩各异的河道以及洪涝区域。# 引用若使用本数据集，请按以下格式引用：>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)# 致谢：众所周知，Xabier Blanch、Franz Wagner以及德累斯顿工业大学的Anette Eltner教授等研究者已发布了诸多优质的水体分割数据集。本研究并非首个相关数据集，其他基准数据集的相关链接可参考如下：[**Elbersdorf/Wesenitz**](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/ONOZRW)、[**RIWA**](https://www.kaggle.com/datasets/franzwagner/river-water-segmentation-dataset)以及[**Kaggle WaterNet/Water Segmentation Dataset**](https://www.kaggle.com/datasets/gvclsu/water-segmentation-dataset)。# 联系方式：- Armin Moghimi：[e-mail]、[e-mail]

搜集汇总

背景与挑战

背景概述

LuFI-RiverSnap是一个包含1092张高分辨率河流场景图像的数据集，涵盖多个国家和复杂水体条件，适用于水体分割算法的研究与开发。