Semantic Segmentation-Based Intermonthly Land Cover Mapping for Graz, Austria and Portorož-Izola-Koper Region, Slovenia (2017-2021)

Description

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.

Steps to reproduce

The land cover dataset was prepared starting with the acquisition of atmospherically corrected Sentinel-2 L2A images. Images were chosen based on minimal cloud coverage and stored as unsigned 16-bit integers. The images were then processed through superpixel segmentation using the Felzenszwalb’s segmentation algorithm to create segmentation masks. The parameters were as follows: - σ = 0.5, - k = 12, - min_size = 15 pixels. The initial ground truth land cover labelling was done for each region using a pretrained UNet model by Esri, available at https://www.arcgis.com/home/item.html?id=afd124844ba84da69c2c533d4af10a58 (accessed on 25 July 2023). Multispectral Sentinel-2 images were normalized, standardized and scaled to 1280 x 1280 pixels before being passed as input to the UNet model. The model's output classification tensor was reshaped to match the size of the original image. The output classification tensor and respective segmentation mask were used to create an initial ground truth image. This process was automated, where each individual segment was selected from the segmentation masks, and then assigned the majority class derived from the corresponding pixels within the classification tensor. Finally, manual label corrections were done due to rare misclassifications from the UNet model. This process was repeated on all images.

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