cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5644746
title: Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery

description: <strong>Power Generation Data Set</strong> This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes. This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images. <br> <strong>Description</strong><br> Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> . This repository contains a total of 1,665 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images. <br> <strong>Content</strong> The following files are contained in this repository: README.md - this file images.zip [2.0GB] - contains 1665 GeoTIFF images segmentation_labels.zip [1.5MB] - contains 1665 JSON files labels.csv [310KB] - contains additional labels for each image: Generation output rate [4],[5] Country Type of fired fuel Latitude and longitude of the power plant Concurrent weather information (temperature, humidity and wind vector) <strong>Acknowledgement</strong> If you use this data set, please cite our publication: Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021. Please refer to this publication for additional information on the data set. The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation. <br> <strong>Author</strong> Joëlle Hanna University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch <br> <strong>References</strong><br> [1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566
DOI: 10.5281/zenodo.5724610
title: Health assessment of plantations based on LiDAR canopy spatial structure parameters

description: The Yellow River Delta (YRD) has China's largest artificial <em>Robinia pseudoacacia</em> forest, which was planted in the late 1970s and suffered extensive dieback in the 1990s. The health grade of the <em>R.pseudoacacia</em> forest (named canopy vigor grade, CVG) could be achieved by using high-resolution images and canopy vigor indicators (CVIs). However, a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass (AGB) of <em>R.pseudoacacia</em> forest. Therefore, this study aims to construct forest health indicators (FHIs) based on canopy spatial structure parameters extracted from LiDAR. The FHIs included Weibull_α (the scale parameter of the Weibull density function that reflects the shape of the tree canopy), VCI (vertical complexity index), sdCC (the standard deviation of canopy cover), H<sub>99</sub> (the 99th percentile height) and cvLAD (the coefficient of variation of leaf area density), and could significantly distinguish three forest health grades (FHG) (<em>p</em> &lt; 0.05). The FHG was positively correlated with forest AGB (<em>r<sub>s</sub></em> = 0.51, <em>p</em> = 0.004), and the similarity value with CVG was 63.33%. The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity, and distinguish forest health status without prior classification information.
DOI: 10.5281/zenodo.5595921
title: Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery

description: Damage estimation status of coconut trees plantation in the Phillippines derived from Sentinel-2 Imagery. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Please refer to our original publication for more details. 0: Uncertain 1: No Data 2: Background class 3: Unchanged coconut plantation 4: Damaged coconut plantation 5: New coconut plantation
description: {"references": ["Rodr\u00edguez, A.C.; Daudt, R.C.; D'Aronco, S.; Schindler, K.; Wegner, J.D. Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery. Remote Sens. 2021, 13, 4302. https://doi.org/10.3390/rs13214302"]}
DOI: 10.5281/zenodo.5595920
title: Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery

description: Damage estimation status of coconut trees plantation in the Phillippines derived from Sentinel-2 Imagery. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Please refer to our original publication for more details. 0: Uncertain 1: No Data 2: Background class 3: Unchanged coconut plantation 4: Damaged coconut plantation 5: New coconut plantation
description: {"references": ["Rodr\u00edguez, A.C.; Daudt, R.C.; D'Aronco, S.; Schindler, K.; Wegner, J.D. Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery. Remote Sens. 2021, 13, 4302. https://doi.org/10.3390/rs13214302"]}
DOI: 10.5281/zenodo.5121321
title: Mosaicked Digital Elevation Models over Nepal

description: The DEMs in this dataset are generated by mosaicking four individual DEM tiles over the 2015 Nepal earthquake epicentral region. The mosaicked DEMs are available in the WGS84 vertical datum. The DEMs represent surface elevation before the occurrence of the 2015 earthquake event. The DEMs are used for topographic phase removal in the Differential InSAR studies.
DOI: 10.5281/zenodo.5095024
title: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks

description: <strong>General information</strong> The dataset consists of 4403 labelled subscenes from 155 Sentinel-2 (S2) Level-1C (L1C) products distributed over the Northern European terrestrial area. Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels subscenes. 6 L1C S2 products were labelled fully. Among other 149 S2 products the most challenging ~10 subscenes per product were selected for labelling. In total the dataset represents 4403 labelled Sentinel-2 subscenes, where each sub-tile is 512 x 512 pixels at 10 m resolution. The dataset consists of around 30 S2 products per month from April to August and 3 S2 products per month for September and October. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations in Northern Europe. The classification pixel-wise map consists of the following categories: 0 – MISSING: missing or invalid pixels; 1 – CLEAR: pixels without clouds or cloud shadows; 2 – CLOUD SHADOW: pixels with cloud shadows; 3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km). 4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km). 5 – UNDEFINED: pixels that the labeler is not sure which class they belong to. The dataset was labelled using Computer Vision Annotation Tool (CVAT) and Segments.ai. With the possibility of integrating active learning process in Segments.ai, the labelling was performed semi-automatically. The dataset limitations must be considered: the data is covering only terrestrial region and does not include water areas; the dataset is not presented in winter conditions; the dataset represent summer conditions, therefore September and October contain only test products used for validation. Current subscenes do not have georeferencing, however, we are working towards including them in next version. More details about the dataset structure can be found in README. <strong>Contributions and Acknowledgements</strong> The data were annotated by Fariha Harun and Olga Wold. The data verification and Software Development was performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold. Methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich.<br> We would like to thank Segments.ai annotation tool for instant and an individual customer support. We are grateful to European Space Agency for reviews and suggestions. We would like to extend our thanks to Prof. Gholamreza Anbarjafari for the feedback and directions.<br> The project was funded by<strong><em> European Space Agency</em></strong>, Contract No. 4000132124/20/I-DT.
DOI: 10.5281/zenodo.5121320
title: Mosaicked Digital Elevation Models over Nepal

description: The DEMs in this dataset are generated by mosaicking four individual DEM tiles over the 2015 Nepal earthquake epicentral region. The mosaicked DEMs are available in the WGS84 vertical datum. The DEMs represent surface elevation before the occurrence of the 2015 earthquake event. The DEMs are used for topographic phase removal in the Differential InSAR studies.
DOI: 10.5281/zenodo.5095023
title: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks

description: <strong>General information</strong> The dataset consists of 4403 labelled subscenes from 155 Sentinel-2 (S2) Level-1C (L1C) products distributed over the Northern European terrestrial area. Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels subscenes. 6 L1C S2 products were labelled fully. Among other 149 S2 products the most challenging ~10 subscenes per product were selected for labelling. In total the dataset represents 4403 labelled Sentinel-2 subscenes, where each sub-tile is 512 x 512 pixels at 10 m resolution. The dataset consists of around 30 S2 products per month from April to August and 3 S2 products per month for September and October. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations in Northern Europe. The classification pixel-wise map consists of the following categories: 0 – MISSING: missing or invalid pixels; 1 – CLEAR: pixels without clouds or cloud shadows; 2 – CLOUD SHADOW: pixels with cloud shadows; 3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km). 4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km). 5 – UNDEFINED: pixels that the labeler is not sure which class they belong to. The dataset was labelled using Computer Vision Annotation Tool (CVAT) and Segments.ai. With the possibility of integrating active learning process in Segments.ai, the labelling was performed semi-automatically. The dataset limitations must be considered: the data is covering only terrestrial region and does not include water areas; the dataset is not presented in winter conditions; the dataset represent summer conditions, therefore September and October contain only test products used for validation. Current subscenes do not have georeferencing, however, we are working towards including them in next version. More details about the dataset structure can be found in README. <strong>Contributions and Acknowledgements</strong> The data were annotated by Fariha Harun and Olga Wold. The data verification and Software Development was performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold. Methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich.<br> We would like to thank Segments.ai annotation tool for instant and an individual customer support. We are grateful to European Space Agency for reviews and suggestions. We would like to extend our thanks to Prof. Gholamreza Anbarjafari for the feedback and directions.<br> The project was funded by<strong><em> European Space Agency</em></strong>, Contract No. 4000132124/20/I-DT.
DOI: 10.5281/zenodo.5020024
title: TimeSpec4LULC: A deep learning-oriented global dataset of MODIS Terra-Aqua multi-spectral time series measured from 2002 to 2021 for LULC mapping and change detection.

description: TimeSpec4LULC is archived in 30 different ZIP files owning the name of the 29 LULC classes (one class is divided into two files since it is too large). Within each ZIP file, there exists a set of seven CSV files, each one corresponding to one of the seven spectral bands. The naming of each file follows this structure: IdOfTheClass_NameOfTheClass_ModisBand.csv<br> For example, for band 1 of the Barren Lands class, the filename is: 01_BarrenLands_MCD09A1b01.csv<br> Inside each CSV file, rows represent the collected pixels for that class. The first 11 columns contain the following metadata:<br> - “IdOfTheClass”: Id of the class.<br> - “NameOfTheClass”: Name of the class.<br> - “IdOfTheLevel0”: Id of the FAO-L0 (i.e., countries).<br> - “IdOfTheLevel1”: Id of the FAO-L1 (i.e., departments, states, or provinces depending on the country).<br> - “IdOfThePixel”: Id of the pixel.<br> - “PurityOfThePixel”: Spatial and inter-annual consensus for this class across multiple land-cover products, i.e., Purity of the pixel.<br> - “DataAvailability”: percentage of non-missing data per band throughout the time series.<br> - “Index_GHM”: average of Global Human Modification index (gHM).<br> - “Lat”: Latitude of the pixel center.<br> - “Lon”: Longitude of the pixel center.<br> - “.geo”: (Longitude, Latitude) of the pixel center with more precision.<br> And, the last 223 columns contain the 223 monthly observations of the time series for one spectral band from 2002-07 to 2021-01. Along with the dataset, an Excel file named 'Countries_Departments_FAO-GAUL' containing the FAO-L0 and the FAO-L1 Ids and names (following the FAO-GAUL standards) is provided.
description: This research has been supported by DETECTOR (A-RNM-256-UGR18 Universidad de Granada/FEDER), LifeWatch SmartEcomountains (LifeWatch-2019-10-UGR-01 Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BBVA DeepSCOP (Ayudas Fundación BBVA a Equipos de Investigación Científica 2018), Ramón y Cajal Programme (RYC-2015-18136), DeepL-ISCO (A-TIC-458-UGR18 Ministerio de Ciencia e Innovación/FEDER), SMART-DASCI (TIN2017-89517-P Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), BigDDL-CET (P18-FR-4961 Ministerio de Ciencia e Innovación/Universidad de Granada/FEDER), RESISTE (P18-RT-1927 Consejería de Economía, Conocimiento, y Universidad from the Junta de Andalucía/FEDER), and Ecopotential (641762 European Commission).
DOI: 10.5281/zenodo.5036990
title: PixBox Sentinel-2 pixel collection for CMIX

description: The <strong>PixBox-S2-CMIX</strong> dataset was used as a validation reference within the first Cloud Masking Inter-comparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) in 2019. The <strong>PixBox-S2-CMIX </strong>pixel collection was existing prior to CMIX and conducted already in 2018. The overarching idea of PixBox is a quantitative assessment of the quality of a pixel classification which is the result of an automated algorithm/procedure. Pixel classification is defined as assigning a certain number of attributes to an image pixel, such as cloud, clear sky, water, land, inland water, flooded, snow etc. Such pixel classification attributes are typically used to further guide higher level processing. The <strong>PixBox dataset </strong>production: trained experienced expert(s) manually classify pixels of an image sensor into a pre-defined detailed set of classes. These are typically different cloud transparencies, cloud shadow, condition of underlying surface (“semi-transparent clouds over snow”, “clouds over bright scattering water”). An average collected dataset includes several 10-thousands of pixels because it has to be representative for all classes, and for various observation and environmental conditions, such as climate zones, sun illumination etc. Quality control of the collected pixels is important in order to detect misclassifications and systematic errors. An auto-associative neural network is trained for this purpose. The PixBox-S2-CMIX dataset is a pixel collection containing 17,351 pixels manually collected from 29 Sentinel-2 A & B Level 1C products. The dataset is spatially, temporally, and thematically well distributed. <strong>PixBox-S2-CMIX dataset</strong> The PixBox-S2-CMIX dataset consists of two two main ZIP files, one holding the pixel collection and description, and another one with all used Sentinel-2 L1C data. The dataset is structured as follows: PixBox-S2-CMIX.zip The collected features (CSV file). A description to all categories and classes, incl. linkage to the used Sentinel-2 L1C products. Sentinel-2_L1C.zip 29 zipped Sentinel-2 Level L1C products <sup>[1]</sup>, used to produce the dataset. <strong>Files </strong> <em>pixbox_sentinel2_cmix_20180425.csv - </em>This file contains all collected pixel information in CSV format. All collected classes are stored as integer values. A description of the categories and definition of the integers to class names is given in the additional description file. <em>pixbox_sentinel2_cmix_20180425_description.txt </em> - This file gives a clear description of the categories and classes. It can be used to convert the class ID numbers, stored in the CSV, to class strings. Additionally, it links the satellite product ID, given in the CSV, to the Sentinel-2 L1C product names. <em>29 Sentinel-2 L1C products in ZIP format.</em> <strong>References</strong> [1] Copernicus Sentinel data 2017/2018
DOI: 10.5281/zenodo.4969147
title: A 30-year climatology (1990-2020) of aerosol optical depth and total column water vapor and ozone over Texas

description: Excel spreadsheet of 30 years of measurements of aerosol optical depth (AOD), precipitable water (TPW) (aka total water vapor) and total ozone over Central Texas from 4 Feb 1990 to 4 Feb 2021. These data represent a WMO climatological mean. The AOD and TPW principal instrument is the first LED sun photometer. The ozone data begin with TOPS, advance to Microtops and culminate with Microtops II.
description: This 30-year report is part of an ongoing project.
DOI: 10.5281/zenodo.4969146
title: A 30-year climatology (1990-2020) of aerosol optical depth and total column water vapor and ozone over Texas

description: Excel spreadsheet of 30 years of measurements of aerosol optical depth (AOD), precipitable water (TPW) (aka total water vapor) and total ozone over Central Texas from 4 Feb 1990 to 4 Feb 2021. These data represent a WMO climatological mean. The AOD and TPW principal instrument is the first LED sun photometer. The ozone data begin with TOPS, advance to Microtops and culminate with Microtops II.
description: This 30-year report is part of an ongoing project.
DOI: 10.6084/m9.figshare.17294033
title: Supplementary Material for: Chemoprevention of Oesophageal Squamous-Cell Carcinoma and Adenocarcinoma: A Multicentre Retrospective Cohort Study

description: <b><i>Background:</i></b> Oesophageal cancer comprises 2 different histological variants: oesophageal squamous-cell carcinoma (ESCC) and adenocarcinoma (EAC). While there are multiple therapeutic options for both types, patients with advanced or metastatic oesophageal cancer still suffer from poor prognosis. <b><i>Aims:</i></b> The study aimed to examine the association between the risk of oesophageal cancer and medications and to estimate the chemopreventive effects of commonly used drugs. <b><i>Methods:</i></b> A multicentre retrospective cohort study was conducted using data from 9 hospital databases of hospitalized patients between 2014 and 2019. The primary outcomes were ESCC and EAC. The association of oesophageal cancer with drug use and clinical factors was evaluated. Odds ratios (ORs) were adjusted for age, sex, Charlson comorbidity index scores, and smoking with/without gastro-oesophageal reflux disease. <b><i>Results:</i></b> The use of proton pump inhibitors (PPIs) (adjusted OR [aOR] 0.48, <i>p</i> &lt; 0.0001), aspirin (aOR 0.32, <i>p</i> &lt; 0.0001), cyclooxygenase-2 inhibitor (COX2I) (aOR 0.70, <i>p</i> = 0.0005), steroid (aOR 0.19, <i>p</i> &lt; 0.0001), statin (aOR 0.43, <i>p</i> &lt; 0.0001), and metformin (aOR 0.42, <i>p</i> &lt; 0.0001) was associated with a lower risk of ESCC than that in non-use. The use of aspirin (aOR 0.33, <i>p</i> = 0.0006) and steroids (aOR 0.54, <i>p</i> = 0.022) was associated with a lower risk of EAC than that in non-use. <b><i>Conclusion:</i></b> COX2Is, statins, metformin, and PPIs could help in prevention of ESCC, and aspirin and steroids may be chemopreventive for both types of oesophageal cancer.
DOI: 10.5517/ccdc.csd.cc25536p
title: CCDC 2000901: Experimental Crystal Structure Determination

description: Related Article: Yuki Wada, Hiroyoshi Ohtsu, Pavel M. Usov, Bun Chan, Krittanun Deekomwong, Masaki Kawano|2021|Inorg.Chem.|60|17858|doi:10.1021/acs.inorgchem.1c02468
DOI: 10.5517/ccdc.csd.cc25537q
title: CCDC 2000902: Experimental Crystal Structure Determination

description: Related Article: Yuki Wada, Hiroyoshi Ohtsu, Pavel M. Usov, Bun Chan, Krittanun Deekomwong, Masaki Kawano|2021|Inorg.Chem.|60|17858|doi:10.1021/acs.inorgchem.1c02468
DOI: 10.5517/ccdc.csd.cc2553cv
title: CCDC 2000906: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc27f0tj
title: CCDC 2068097: Experimental Crystal Structure Determination

description: Related Article: Midori Akiyama|2021|CSD Communication|||
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DOI: 10.5517/ccdc.csd.cc27g6nl
title: CCDC 2069239: Experimental Crystal Structure Determination

description: Related Article: Rakesh Mazumdar, Shankhadeep Saha, Bapan Samanta, Biplab Mondal|2021|Inorg.Chem.|60|18024|doi:10.1021/acs.inorgchem.1c02606
DOI: 10.5517/ccdc.csd.cc27g6pm
title: CCDC 2069240: Experimental Crystal Structure Determination

description: Related Article: Rakesh Mazumdar, Shankhadeep Saha, Bapan Samanta, Biplab Mondal|2021|Inorg.Chem.|60|18024|doi:10.1021/acs.inorgchem.1c02606
DOI: 10.5517/ccdc.csd.cc27qgwb
title: CCDC 2077182: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc27qgxc
title: CCDC 2077183: Experimental Crystal Structure Determination

description: Related Article: Peng-Fei Chen, Bo Zhou, Peng Wu, Binju Wang, Long-Wu Ye|2021|Angew.Chem.,Int.Ed.|60|27164|doi:10.1002/anie.202113464
DOI: 10.5517/ccdc.csd.cc27qgyd
title: CCDC 2077184: Experimental Crystal Structure Determination

description: Related Article: Peng-Fei Chen, Bo Zhou, Peng Wu, Binju Wang, Long-Wu Ye|2021|Angew.Chem.,Int.Ed.|60|27164|doi:10.1002/anie.202113464
DOI: 10.5517/ccdc.csd.cc27qgzf
title: CCDC 2077185: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc27qk3n
title: CCDC 2077251: Experimental Crystal Structure Determination

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