Semantic Segmentation-Based Intermonthly Land Cover Mapping for Graz, Austria and Portorož-Izola-Koper Region, Slovenia (2017-2021)
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This dataset provides intermonthly mapping of land cover changes from the period 2017 to 2021 for the region of Graz, Austria, and the coastal region of Portorož, Izola, and Koper in Slovenia. In the Graz region, images were procured within the WGS84 bounding box defined by the coordinates [15.390816°, 46.942176°, 15.515785°, 47.015961°], accounting for a total of 40 images. The region of Portorož, Izola, and Koper in Slovenia, contained within the WGS84 bounding box [13.590260°, 45.506948°, 13.744411°, 45.554449°], yielded a total of 41 images. All images obtained maintain minimal cloud coverage and have a spatial resolution of 10 meters. Comprised within this dataset are raw Sentinel-2 images in numpy format in conjunction with True Color (RGB) images in PNG format, each procured from Sentinel Hub. The ground truth label data is preserved in numpy format and has been additionally rendered in color-coded PNGs. The dataset also includes land cover maps predicted for the test set (2020-2021), as outlined in the research article, available at https://doi.org/10.3390/s23146648. Each file adheres to a nomenclature denoting the year and the month (e.g., 2017_1 corresponds to an image/ground truth/prediction for the January 2017). Initial ground truth was obtained using the ESRI's UNet model, available at https://www.arcgis.com/home/item.html?id=afd124844ba84da69c2c533d4af10a58 (accessed on 25 July 2023). Subsequent manual corrections were administered to enhance the accuracy and integrity of the data. The Graz region contains 12 distinct classes, while the region of Portorož-Izola-Koper comprises 13 classes. The dataset is structured as follows: - 'classes.txt' contains a list of land cover classes, - '/data' hosts the Sentinel-2 imagery, -- '/data/numpy' retains Sentinel-2 images featuring 13 basic spectral layers (B01–B12) in numpy format, -- '/data/true_color_png' stores True Color (RGB) images in PNG format, - '/ground_truth' contains ground truth, -- '/ground_truth/numpy' houses ground truth in numpy format with values ranging from 0 to 14 representing distinct classes, -- '/ground_truth/color_labeled_png' contains color-labeled images in PNG format. - '/predictions' contains predicted land cover maps for the test set from the associated research paper, -- '/predictions/numpy' has predictions in numpy format with values ranging from 0 to 14 representing distinct classes, -- '/predictions/color_labeled_png' contains color-labeled images in PNG format. All these directories further include subdirectories '/graz' and '/portoroz_izola_koper' corresponding to the two regions covered in the datasets. Acknowledgments: Should you find this dataset useful in your work, we kindly request that you acknowledge its origin by citing the following article: Kavran, D.; Mongus, D.; Žalik, B.; Lukač, N. Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery. Sensors 2023, 23, 6648. https://doi.org/10.3390/s23146648.
本数据集提供了奥地利格拉茨（Graz）地区，以及斯洛文尼亚波特罗什（Portorož）、伊佐拉（Izola）与科佩尔（Koper）沿海区域2017年至2021年的逐月土地覆盖变化映射。格拉茨区域的影像获取范围为WGS84坐标系下的边界框（bounding box）[15.390816°, 46.942176°, 15.515785°, 47.015961°]，共计包含40幅影像。斯洛文尼亚波特罗什、伊佐拉与科佩尔区域的影像获取范围为WGS84坐标系下的边界框（bounding box）[13.590260°, 45.506948°, 13.744411°, 45.554449°]，共计包含41幅影像。所有获取的影像均具有极低云量，空间分辨率为10米。本数据集包含源自Sentinel Hub的原始Sentinel-2影像（numpy格式）与真彩色（RGB）PNG格式影像。地面真值标签数据以numpy格式存储，并额外导出为带颜色编码的PNG图像。数据集还包含如研究文章所述的测试集（2020-2021年）预测土地覆盖图，相关文章链接为https://doi.org/10.3390/s23146648。 所有文件均遵循标注年份与月份的命名规范（例如，2017_1代表2017年1月对应的影像、地面真值与预测结果）。初始地面真值通过ESRI的UNet模型生成，模型链接为https://www.arcgis.com/home/item.html?id=afd124844ba84da69c2c533d4af10a58（访问时间：2023年7月25日）。后续通过人工修正提升了数据的准确性与完整性。 格拉茨区域包含12个不同的土地覆盖类别，而波特罗什-伊佐拉-科佩尔区域则包含13个类别。数据集的组织结构如下： - 'classes.txt'：存储土地覆盖类别列表； - '/data'：存放Sentinel-2影像， -- '/data/numpy'：保留包含13个基础光谱波段（B01–B12）的numpy格式Sentinel-2影像； -- '/data/true_color_png'：存储真彩色（RGB）PNG格式影像； - '/ground_truth'：存放地面真值数据， -- '/ground_truth/numpy'：存储numpy格式的地面真值，其取值范围为0至14，分别对应不同类别； -- '/ground_truth/color_labeled_png'：包含带颜色标注的PNG格式图像； - '/predictions'：存放关联研究论文中测试集的预测土地覆盖图， -- '/predictions/numpy'：存储numpy格式的预测结果，其取值范围为0至14，分别对应不同类别； -- '/predictions/color_labeled_png'：包含带颜色标注的PNG格式图像。 所有目录均进一步包含子目录'/graz'与'/portoroz_izola_koper'，分别对应数据集覆盖的两个研究区域。 致谢：若本数据集对您的研究工作有所帮助，恳请您引用以下文章以指明数据集来源：Kavran, D.; Mongus, D.; Žalik, B.; Lukač, N. 基于图神经网络的卫星影像时空土地覆盖制图方法. Sensors 2023, 23, 6648. https://doi.org/10.3390/s23146648.
