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                "en": "This dataset was used in **Improved Difference the Images for Change Detection Classifiers in SAR Imagery Using Deep Learning** paper. The more in depth description of the dataset is available in the paper.\n\nThe paper is undergoing peer-review, however a preprint version of the paper is available on arXiv: [https://arxiv.org/abs/2303.17835](https://arxiv.org/abs/2303.17835)\n\n# License\n\nThe dataset is created by combining multiple open data sources. The licenses for the data sources are listed in the acknowledgements section. By using this dataset, you must comply to the licenses of the data sources.\n\n# Documentation\n\nThe dataset uses compressed TFRecords to store the dataset samples.\n[https://www.tensorflow.org/tutorials/load_data/tfrecord](https://www.tensorflow.org/tutorials/load_data/tfrecord)\n\nExample code for parsing and using the dataset can be found from:\n[https://github.com/janne-alatalo/sar-change-detection](https://github.com/janne-alatalo/sar-change-detection)\n\nOne sample is constructed from the following features:\n\n`image_location`:  \nGeographic centroid point of the sample in Well-known text representation in\n`EPSG:4326` coordinate system. The string is generated with\n`ogr.Geometry.ExportToWkt()` method which uses the `POINT(LATITUDE LONGITUDE)`\nformat.\n\n`dem_rast`:  \nDigital Elevation map from the location that is projected to same projection\nwindow with the SAR images (same 512x512 resulution with same pixel\ngeographical alignment)\n\n`forestmask`:  \nBinary mask (`1 == forest` or `0 == non-forest`) that shows the forested areas in\nthe scene. The pixels have same alignment as the SAR images and DEM. This data\nwas not used in the final results in the paper, however during the development\nphase an experiment was conducted where the loss function weighted forested\narea pixels more than non-forest pixels. The result was that the custom loss\nfunction was not much different from vanilla MSE, thus the result was not\nreported in the paper. The data is acquired from Finnish Forest Centre:\n[https://aineistot.metsaan.fi/avoinmetsatieto/Metsamaski/Maakunta/](https://aineistot.metsaan.fi/avoinmetsatieto/Metsamaski/Maakunta/)\n\n`target_image`:  \nThe \"newest\" image in the sample (I_t). This is the target in the neural\nnetwork training. Both bands  are in the same image, meaning that the image\nshape is (2, 512, 512) with band order (VV, VH).\n\n`target_image_data_take_id`:  \nThe data take id of the `target_image`. Identifies the data take from which the\ntarget image is cropped from.\n\n`target_image_start_time`:  \nThe data take start time. Identifies the data take from which the target image\nis cropped from and tells when the image is acquired. The timestamp is created\nwith Python standard library `datetime.isoformat()` method.\n[https://docs.python.org/3/library/datetime.html#datetime.datetime.isoformat](https://docs.python.org/3/library/datetime.html#datetime.datetime.isoformat)\n\n`target_image_incidence_angle`:  \nIncidence angle at the centroid point of the target SAR image.\n\n`target_image_platform_heading`:  \nSatellite fly direction during the acquisition.\n\n`target_image_precipitations`:  \nRain amounts from the acquisition day and three previous days from the location.\n\n`target_image_temperature`:  \nMean temperature from the acquisition day from the location.\n\n`target_image_snow_depth`:  \nSnow depth from the acquisition day at the location.\n\n`input_image_stack`:  \nThe input images that are stacked in the band dimension (8, 512, 512).\n```\nI_{t - 4} VV band is input_image_stack[0, :, :]\nI_{t - 4} VH band is input_image_stack[1, :, :]\n...\nI_{t - 1} VV band is input_image_stack[6, :, :]\nI_{t - 1} VH band is input_image_stack[7, :, :]\n```\n\nAll of the `target_image_*` features have matching `input_image_*s` features,\nexcept that the `input_image_*` features are arrays with length 4. The input\nimages are ordered from oldest to newest:\n`input_image_start_time[0] < input_image_start_time[-1]`.\n\n\nFurthermore, the validatation dataset samples also include the statistical\nsimulated changes that are described in the paper. The dataset includes the\nfollowing additional features:\n\n`simulated_change_image`:  \ntarget_image that includes the simulated statistical change.\n\n`simulated_change_mask`:  \n512 x 512 image that indicates the simulated change pixels with non 0 values.\nNOTE that the mask value can be anything between 0 and num_simulated_changes. 1\nindicates the pixels associated with the first simulated change, 2 with the\nsecond and so on...\n\n`num_simulated_changes`:  \nIndicates the number of simulated changes in the simulated_change_image.\n\n`img_superpixel_map` and `img_superpixel_sets`:  \nHelper features for the simulated change algorithm implementations. The\nfeatures are a result from that SAR images have different resolutions in\ndifferent directions, however due to technical decision the SAR images in the\ntraining dataset have same resolutions in both width and height directions.\nTherefore, the images in the dataset have many neighboring pixels with exact\nsame value. These features keep track of these pixels so that the simulated\nchanges can be made to look realistic.\n\n# Acknowledgements\n\nThis dataset contains modified Copernicus Sentinel data 2020-2021.  \nThe data is governed by the Legal Notice on the use of Copernicus Sentinel Data and Service Information  \n[https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice](https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice)\n\nThis dataset contains weather data from Finnish Meteorological Institute,  \nlicensed under Creative Commons Attribution 4.0 International license (CC BY 4.0)  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n\nThis dataset contains geographical data (digital elevation map) from National Land Survey of Finland,  \nlicensed under Creative Commons Attribution 4.0 International license (CC BY 4.0)  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)\n\nThis dataset contains geographical data (forest mask) from Finnish Forest Centre,  \nlicensed under Creative Commons Attribution 4.0 International license (CC BY 4.0)  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)"
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}