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integration_instructions Research softwarekeyboard_double_arrow_right Software 2024 EnglishPublisher:Zenodo Authors: Aucone, Emanuele;Aucone, Emanuele;The repo contains the code for the simulation environment and for the NMPC that runs on a real drone. -----------------------------------------------------Simulation environment for traversing complliant obstacles: - System Requirements: Ubuntu 18.04 (or 20.04) with ROS Melodic (or Noetic). - Descripion and Usage: The simulation environment is created for testing physical interaction strategies for traversing compliant environments. The main building block is RotorS package (https://github.com/ethz-asl/rotors_simulator), as we used it to create a model of our drone (URDF) and to have sensors and plugins for state estimation and flight. We add a hinged door as compliant environment; fr the compliant behavior we use a plugin for rotational joints with spring reaction (https://github.com/aminsung/gazebo_joint_torsional_spring_plugin). We build our Nonlinear Model Predictive Control for the traversal task,; the MPC is adapted from https://github.com/uzh-rpg/rpg_mpc, which is based on ACADO and qpoases. Parameters of the optimization-based controller can be changed, and are loaded when launched. The flight control architecture, which is based on https://github.com/uzh-rpg/rpg_quadrotor_control, uses the MPC in an autopilot fashion. All these mentioned repos can directly be installed from ours. Further detailed explanations of the different components can be found on the linked packages. The NMPC controller node further needs to receive a reference trajectory command to start, which is done with a rosservice call on the service 'follow_trajectory', where the speed has to be specified. Example: 'rosservice call /your_drone/follow_trajectory "reference_velocity: x: -0.15 y: 0.0 z: 0.0" '----------------------------------------------------- Nonlinear Model Predictive Control (NMPC) strategy for traversal (aerial physical interaction) with compliant obstacles - to run on real drones: - System Requirements: Ubuntu 18.04 (or 20.04) with ROS Melodic (or Noetic). On our drone the code runs on the Khadas Vim3 Pro. - Descripion and Usage: The controller node needs to be included in a control loop. It has to subscribe to a state estimator (drone's full state can be obtained with a motion capture system or, like in our case, from a tracking camera Intel Realsense T261 https://www.intelrealsense.com/wp-content/uploads/2019/09/Intel_RealSense_Tracking_Camera_Datasheet_Rev004_release.pdf?_ga=2.85385625.1408955752.1709226166-1763584868.1709226166) and to a force sensor (in our case Medusa F/T sensor from Bota System AG https://www.botasys.com/force-torque-sensors/medusa). We suggest to filter the force sensor readings to have smoother measurements. The controller node sends thrust and attitude commands to the low-level controller (Flight Controller running Betaflight https://betaflight.com/). The MPC is adapted from https://github.com/uzh-rpg/rpg_mpc, which is based on ACADO and qpoases. Parameters of the optimization-based controller can be changed, and are loaded when launched. The whole flight control architecture, as well as the HW part, is built upon https://github.com/uzh-rpg/rpg_quadrotor_control, where the controller is used in an autopilot fashion. This repo must be installed and run on your drone. Our NMPC can then be cloned and added to your workspace. A very detailed explanation of both HW and SW requirements is already provided there. The NMPC controller node further needs to receive a reference trajectory command to start, which is done with a rosservice call on the service 'follow_trajectory', where the speed has to be specified. Example: 'rosservice call /khadas_drone/follow_trajectory "reference_velocity: x: -0.15 y: 0.0 z: 0.0" '
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Zenodo Funded by:EC | FORESTPOLICYEC| FORESTPOLICYAuthors: Hodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; +2 AuthorsHodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; Valentim, Judson; Garrett, Rachael;Datasets for the deep learning and regression analysis for the manuscript "Deep learning-based cattle counts on satellite imagery offer evidence regarding land use and policy impact in the Brazilian Amazon.".
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024 EnglishPublisher:Zenodo Authors: Gröbner, Julian;Gröbner, Julian;This item consists of a dataset of direct solar irradiance data and atmospheric optical depth in the spectral range from 300 nm to 2150 nm obtained from a BTS/UVNIR and a BTS/IR specroradiometer in the period 1st January 2022 to 31 December 2023. The instruments were operated for the most part at the PMOD/WRC, while several weeks of measurements in September 2022 were performed at the high altitude observatory of Izaña, Tenerife, Canary Islands.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Zenodo Funded by:EC | FORESTPOLICYEC| FORESTPOLICYAuthors: Hodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; +2 AuthorsHodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; Valentim, Judson; Garrett, Rachael;This repository contains datasets relevant to CSRNet-based cattle counts and stocking rate estimations in the Brazilian Amazon and related variables relevant for the publication with the name "Deep learning-based cattle counts on satellite imagery offer evidence regarding land use and policy impact in the Brazilian Amazon." Please refer to the _README.rtf file for further details.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Data Outputs Funded by:EC | SUSTUNTECH, EC | ANERIS, EC | AtlantECO +6 projectsEC| SUSTUNTECH ,EC| ANERIS ,EC| AtlantECO ,EC| AGENSI ,EC| MISSION ATLANTIC ,SNSF| Untersuchungen zu möglichen Auswirkungen des Anbaus von transgenen Bacillus thuringiensis (Bt) Maissorten im Feld auf Bodenökosysteme. ,SNSF| Molecular evolution and ecology of Foraminifera and related protists ,ANR| TAD ,EC| FutureMARESAuthors: ICES;ICES;This is a published version of the WGMLEARN literature collection currently managed as a Zotero group library. That library is managed and curated by members of WGMLEARN and aims to be a collection of all the published works at the intersection of machine learning and marine science.The Zotero library is continuously updated, but a static instance of all its contents from May 2023 can be downloaded here for use in reference management software.Custom keywords are included with each item; these allow for classification by data type (data:*), machine learning task (task:*), and algorithm (method:*). Other keywords are included for information but they are not guaranteed to be applied consistently.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMommert, Michael; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge/ERA-5: BigEarthNet Extended with Geographical and Environmental Data/Environmental Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. This archive contains the environmental data of ben-ge, which were extracted from the ERA-5 global reanalysis. Data Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations). Environmental data are available in the file ben-ge_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters: * atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar] * temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K] * temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K] * wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s] * wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%] * relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%] as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details. Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/era-5 requires 80 MB of space. Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows: | |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) ... More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge If you use data contained in this archive, please cite the following paper: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 9visibility views 9 download downloads 3 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMommert, Michael; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge/ESAWorldCover: BigEarthNet Extended with Geographical and Environmental Data/Land-use/land-cover Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. This archive contains the land-use/land-cover data of ben-ge, which were extracted from the ESA WorldCover service. Data Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (https://esa-worldcover.org). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch. Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf) (page 15). The file ben-ge_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups. Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- esaworldcover/ (land-use/land-cover data) |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif ... To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/esaworldcover requires 8.7 GB of space. Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows: | |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) ... More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge If you use data contained in this archive, please cite the following paper: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 12visibility views 12 download downloads 3 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMichael Mommert; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge/DEM: BigEarthNet Extended with Geographical and Environmental Data/Elevation Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. This archive contains the digital elevation model (DEM) data of ben-ge, which were extracted from the Copernicus Digital Elevation Model (GLO-30). Data Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground. Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level. Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge_meta.csv (ben-ge meta data) |--- dem/ (digital elevation model data) |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif ... To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/DEM requires 17.2 GB of space. Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows: | |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) ... More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge If you use data contained in this archive, please cite the following paper: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 11visibility views 11 download downloads 4 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMommert, Michael; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge-8k: BigEarthNet Extended with Geographical and Environmental Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge-8k is a small-scale multimodal dataset for Earth observation that is a subset of the ben-ge dataset (https://github.com/HSG-AIML/ben-ge), which in turn serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. ben-ge-8k contains 8000 patches out of 590,326 patches in the full ben-ge dataset. These 8000 patches were sampled in such a way that for each of the 8 most common ESA WorldCover land-use/land-cover classes (tree cover, shrubland, grassland, cropland, built-up, bare/sparse vegetation, permanent water bodies, herbaceous wetland), we sampled 1000 patches randomly and used the fractional coverage of this class as a weight in the sampling process. As a result, these classes are slightly more balanced in ben-ge-8k than in the full dataset. Data Modalities and Products Meta Data Relevant meta data for the ben-ge-8k dataset are compiled in the file ben-ge-8k_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). Digital Elevation Model (Copernicus DEM GLO-30) DEM data are contained in the dem/ directory of this archive. Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground. Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level. Land-use/Land-cover Data (ESA WorldCover) Land-use/land-cover data are contained in the esaworldcover/ directory of this archive. Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (https://esa-worldcover.org). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch. Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf) (page 15). The file ben-ge-8k_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups. Environmental Data (ERA-5) Weather data are contained in the ben-ge-8k_era-5.csv file. Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations). Environmental data are available in the file ben-ge-8k_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters: * atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar] * temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K] * temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K] * wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s] * wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%] * relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%] as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details. Seasonal Encoding To capture the season at the time of observation, we apply a non-linear encoding that scale the date of the observation into the interval [0, 1], referring to [winter, summer] solstice. For any given date, we derive the fractional year and shift it by 9 days such that 21 June has the fractional year 0.5 and 22 December has the fractional year 0 or 1. To account for this ambiguity and the periodicity of the seasons, we modulate the fractional year with a sine function such that 21 June leads to a seasonal encoding of 1 and 22 December leads to a seasonal encoding of 0. Seasonal encodings are provided by the column season in the ben-ge-8k_meta.csv file. Season values cover the interval [0,1] as a continuous variable where 1 refers to summer solstice and 0 refers to winter solstice. Climate zone classification (Beck et al. 2018) Patch-based climate zone classifications, based on the Köppen-Geiger scheme, were extracted from Beck et al. (2018) (https://www.nature.com/articles/sdata2018214), utilizing their present-day 1-km resolution map. Due to geographical focus of BigEarthNet on Europe, only 11 out of 27 different classes are present in this dataset. Please note that patches that are fully covered by surface water have no climate zone class assigned to them (class label equals zero in this case). Labels are encoded as discrete integer values that follow the schema introduced by Beck et al. 2018 in their legend.txt file that is included here: https://doi.org/10.6084/m9.figshare.6396959. Climate zone classification labels are provided by the column climatezone in the ben-ge-8k_meta.csv file. File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge-8k_meta.csv (ben-ge-8k meta data) |--- ben-ge-8k_era-5.csv (ben-ge-8k environmental data) |--- ben-ge-8k_esaworldcover.csv (patch-wise ben-ge-8k land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) | ... |--- splits/ |--- ben-ge-8k_test.csv (index file for test split, 10%) |--- ben-ge-8k_validation.csv (index file for validation split, 10%) |--- ben-ge-8k_train.csv (index file for training splits, 80%) Once unpacked, ben-ge-8k requires 4.2 GB of space. More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge-8k If you use data contained in this archive, please cite the following two papers: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. G. Sumbul, A. d. Wall, T. Kreuziger, F. Marcelino, H. Costa, P. Benevides, M. Caetano, B. Demir, V. Markl, "BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval", IEEE Geoscience and Remote Sensing Magazine, 2021, doi: 10.1109/MGRS.2021.3089174.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 13visibility views 13 download downloads 4 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023 EnglishPublisher:Zenodo Funded by:SNSF | Bioaccumulation and biotr...SNSF| Bioaccumulation and biotransformation of organic xenobiotics in aquatic organismsAuthors: Kiefer, Karin; Müller, Adrian; Singer, Heinz; Hollender, Juliane;Kiefer, Karin; Müller, Adrian; Singer, Heinz; Hollender, Juliane;This is the collection associated with list S60 SWISSPEST19 on the NORMAN Suspect List Exchange. https://www.norman-network.com/nds/SLE/ Swiss pesticides (plant protection products) and metabolites from Kiefer et al 2019 (Eawag), Tables SI-B 1 and 2, DOI: 10.1016/j.watres.2019.114972 Update 25 April 2020: fixed many naming issues in xlsx and csv file. No structural information changed. 25 Mar 2023: fixed date CAS. 25 May 2023: fixed non-live CIDs to live CIDs. 6 Jul 2023: added NOA 413161 structures and transformations file. For full information refer to original publication: DOI: 10.1016/j.watres.2019.114972
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For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 4Kvisibility views 4,163 download downloads 3,497 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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integration_instructions Research softwarekeyboard_double_arrow_right Software 2024 EnglishPublisher:Zenodo Authors: Aucone, Emanuele;Aucone, Emanuele;The repo contains the code for the simulation environment and for the NMPC that runs on a real drone. -----------------------------------------------------Simulation environment for traversing complliant obstacles: - System Requirements: Ubuntu 18.04 (or 20.04) with ROS Melodic (or Noetic). - Descripion and Usage: The simulation environment is created for testing physical interaction strategies for traversing compliant environments. The main building block is RotorS package (https://github.com/ethz-asl/rotors_simulator), as we used it to create a model of our drone (URDF) and to have sensors and plugins for state estimation and flight. We add a hinged door as compliant environment; fr the compliant behavior we use a plugin for rotational joints with spring reaction (https://github.com/aminsung/gazebo_joint_torsional_spring_plugin). We build our Nonlinear Model Predictive Control for the traversal task,; the MPC is adapted from https://github.com/uzh-rpg/rpg_mpc, which is based on ACADO and qpoases. Parameters of the optimization-based controller can be changed, and are loaded when launched. The flight control architecture, which is based on https://github.com/uzh-rpg/rpg_quadrotor_control, uses the MPC in an autopilot fashion. All these mentioned repos can directly be installed from ours. Further detailed explanations of the different components can be found on the linked packages. The NMPC controller node further needs to receive a reference trajectory command to start, which is done with a rosservice call on the service 'follow_trajectory', where the speed has to be specified. Example: 'rosservice call /your_drone/follow_trajectory "reference_velocity: x: -0.15 y: 0.0 z: 0.0" '----------------------------------------------------- Nonlinear Model Predictive Control (NMPC) strategy for traversal (aerial physical interaction) with compliant obstacles - to run on real drones: - System Requirements: Ubuntu 18.04 (or 20.04) with ROS Melodic (or Noetic). On our drone the code runs on the Khadas Vim3 Pro. - Descripion and Usage: The controller node needs to be included in a control loop. It has to subscribe to a state estimator (drone's full state can be obtained with a motion capture system or, like in our case, from a tracking camera Intel Realsense T261 https://www.intelrealsense.com/wp-content/uploads/2019/09/Intel_RealSense_Tracking_Camera_Datasheet_Rev004_release.pdf?_ga=2.85385625.1408955752.1709226166-1763584868.1709226166) and to a force sensor (in our case Medusa F/T sensor from Bota System AG https://www.botasys.com/force-torque-sensors/medusa). We suggest to filter the force sensor readings to have smoother measurements. The controller node sends thrust and attitude commands to the low-level controller (Flight Controller running Betaflight https://betaflight.com/). The MPC is adapted from https://github.com/uzh-rpg/rpg_mpc, which is based on ACADO and qpoases. Parameters of the optimization-based controller can be changed, and are loaded when launched. The whole flight control architecture, as well as the HW part, is built upon https://github.com/uzh-rpg/rpg_quadrotor_control, where the controller is used in an autopilot fashion. This repo must be installed and run on your drone. Our NMPC can then be cloned and added to your workspace. A very detailed explanation of both HW and SW requirements is already provided there. The NMPC controller node further needs to receive a reference trajectory command to start, which is done with a rosservice call on the service 'follow_trajectory', where the speed has to be specified. Example: 'rosservice call /khadas_drone/follow_trajectory "reference_velocity: x: -0.15 y: 0.0 z: 0.0" '
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Zenodo Funded by:EC | FORESTPOLICYEC| FORESTPOLICYAuthors: Hodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; +2 AuthorsHodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; Valentim, Judson; Garrett, Rachael;Datasets for the deep learning and regression analysis for the manuscript "Deep learning-based cattle counts on satellite imagery offer evidence regarding land use and policy impact in the Brazilian Amazon.".
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024 EnglishPublisher:Zenodo Authors: Gröbner, Julian;Gröbner, Julian;This item consists of a dataset of direct solar irradiance data and atmospheric optical depth in the spectral range from 300 nm to 2150 nm obtained from a BTS/UVNIR and a BTS/IR specroradiometer in the period 1st January 2022 to 31 December 2023. The instruments were operated for the most part at the PMOD/WRC, while several weeks of measurements in September 2022 were performed at the high altitude observatory of Izaña, Tenerife, Canary Islands.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.10497192&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Zenodo Funded by:EC | FORESTPOLICYEC| FORESTPOLICYAuthors: Hodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; +2 AuthorsHodel, Leonie; Wegner, Jan Dirk; Sainte Fare Garnot, Vivien; Rocha-Gomes, Francisco; Valentim, Judson; Garrett, Rachael;This repository contains datasets relevant to CSRNet-based cattle counts and stocking rate estimations in the Brazilian Amazon and related variables relevant for the publication with the name "Deep learning-based cattle counts on satellite imagery offer evidence regarding land use and policy impact in the Brazilian Amazon." Please refer to the _README.rtf file for further details.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.10674946&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2024Publisher:Data Outputs Funded by:EC | SUSTUNTECH, EC | ANERIS, EC | AtlantECO +6 projectsEC| SUSTUNTECH ,EC| ANERIS ,EC| AtlantECO ,EC| AGENSI ,EC| MISSION ATLANTIC ,SNSF| Untersuchungen zu möglichen Auswirkungen des Anbaus von transgenen Bacillus thuringiensis (Bt) Maissorten im Feld auf Bodenökosysteme. ,SNSF| Molecular evolution and ecology of Foraminifera and related protists ,ANR| TAD ,EC| FutureMARESAuthors: ICES;ICES;This is a published version of the WGMLEARN literature collection currently managed as a Zotero group library. That library is managed and curated by members of WGMLEARN and aims to be a collection of all the published works at the intersection of machine learning and marine science.The Zotero library is continuously updated, but a static instance of all its contents from May 2023 can be downloaded here for use in reference management software.Custom keywords are included with each item; these allow for classification by data type (data:*), machine learning task (task:*), and algorithm (method:*). Other keywords are included for information but they are not guaranteed to be applied consistently.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMommert, Michael; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge/ERA-5: BigEarthNet Extended with Geographical and Environmental Data/Environmental Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. This archive contains the environmental data of ben-ge, which were extracted from the ERA-5 global reanalysis. Data Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations). Environmental data are available in the file ben-ge_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters: * atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar] * temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K] * temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K] * wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s] * wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%] * relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%] as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details. Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/era-5 requires 80 MB of space. Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows: | |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) ... More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge If you use data contained in this archive, please cite the following paper: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 9visibility views 9 download downloads 3 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMommert, Michael; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge/ESAWorldCover: BigEarthNet Extended with Geographical and Environmental Data/Land-use/land-cover Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. This archive contains the land-use/land-cover data of ben-ge, which were extracted from the ESA WorldCover service. Data Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (https://esa-worldcover.org). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch. Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf) (page 15). The file ben-ge_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups. Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- esaworldcover/ (land-use/land-cover data) |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif ... To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/esaworldcover requires 8.7 GB of space. Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows: | |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) ... More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge If you use data contained in this archive, please cite the following paper: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 12visibility views 12 download downloads 3 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMichael Mommert; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge/DEM: BigEarthNet Extended with Geographical and Environmental Data/Elevation Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge is a multimodal dataset for Earth observation (https://github.com/HSG-AIML/ben-ge) that serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. This archive contains the digital elevation model (DEM) data of ben-ge, which were extracted from the Copernicus Digital Elevation Model (GLO-30). Data Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground. Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level. Relevant meta data for the ben-ge dataset are compiled in the file ben-ge_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge_meta.csv (ben-ge meta data) |--- dem/ (digital elevation model data) |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif ... To properly conserve the file and directory structure of the ben-ge dataset, please place this archive file on the root level of the ben-ge dataset and then unpack it. Once unpacked, ben-ge/DEM requires 17.2 GB of space. Other data modalities from ben-ge (as well as Sentinel-1/2 data as provided by BigEarthNet, https://bigearth.net/#downloads), may be added as required. For reference, the recommended structure for the full dataset looks as follows: | |--- ben-ge_meta.csv (ben-ge meta data) |--- ben-ge_era-5.csv (ben-ge environmental data) |--- ben-ge_esaworldcover.csv (patch-wise ben-ge land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) ... More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge If you use data contained in this archive, please cite the following paper: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023.
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For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!visibility 11visibility views 11 download downloads 4 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023Publisher:Zenodo Funded by:SNSF | Self-Supervised Learning ...SNSF| Self-Supervised Learning for Earth Observation: Leveraging a wealth of multi-modal dataMommert, Michael; Kesseli, Nicolas; Hanna, Joelle; Scheibenreif, Linus; Borth, Damian; Demir, Begüm;ben-ge-8k: BigEarthNet Extended with Geographical and Environmental Data M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. ben-ge-8k is a small-scale multimodal dataset for Earth observation that is a subset of the ben-ge dataset (https://github.com/HSG-AIML/ben-ge), which in turn serves as an extension to the BigEarthNet dataset. ben-ge complements the Sentinel-1/2 data contained in BigEarthNet by providing additional data modalities: * elevation data extracted from the Copernicus Digital Elevation Model GLO-30; * land-use/land-cover data extracted from ESA Worldcover; * climate zone information extracted from Beck et al. 2018; * environmental data concurrent with the Sentinel-1/2 observations from the ERA-5 global reanalysis; * a seasonal encoding. ben-ge-8k contains 8000 patches out of 590,326 patches in the full ben-ge dataset. These 8000 patches were sampled in such a way that for each of the 8 most common ESA WorldCover land-use/land-cover classes (tree cover, shrubland, grassland, cropland, built-up, bare/sparse vegetation, permanent water bodies, herbaceous wetland), we sampled 1000 patches randomly and used the fractional coverage of this class as a weight in the sampling process. As a result, these classes are slightly more balanced in ben-ge-8k than in the full dataset. Data Modalities and Products Meta Data Relevant meta data for the ben-ge-8k dataset are compiled in the file ben-ge-8k_meta.csv. This file resides on the root level of this archive and contains the following data for each patch: * patch_id: the Sentinel-2 patch id, which plays a central role for cross-referencing different data modalities for individual patches; * patch_id_s1: the Sentinel-1 patch id for this specific patch; * timestamp_s2: the timestamp for the Sentinel-2 observation; * timestamp_s1: the timestamp for the Sentinel-1 observation; * season_s2: the seasonal encoding (see below) for the time of the Sentinel-2 observation; * season_s1: the seasonal encoding (see below) for the time of the Sentinel-1 observation; * lon: longitude (WGS-84) of the center of the patch [degrees]; * lat: latitude (WGS-84) of the center of the patch [degrees]; * climatezone: integer value indicating the climate zone based on Beck et al. 2018 (see below for details). Digital Elevation Model (Copernicus DEM GLO-30) DEM data are contained in the dem/ directory of this archive. Topographic maps are generated based on the global Copernicus Digital Elevation Model (GLO-30) (https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model). Relevant GLO-30 map tiles from the 2021 data release were downloaded through AWS (https://registry.opendata.aws/copernicus-dem/), reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches and interpolated with bilinear resampling to 10 m resolution on the ground. Elevation data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _dem.tif. Each file contains a single band with 16-bit integer values that refer to the elevation of that pixel over sea level. Land-use/Land-cover Data (ESA WorldCover) Land-use/land-cover data are contained in the esaworldcover/ directory of this archive. Land-use/land-cover map tiles matching the Sentinel-1/2 patches were extracted from ESA WorldCover (https://esa-worldcover.org). Relevant tiles were downloaded and reprojected into the coordinate frame of the corresponding Sentinel-1/2 patches. WorldCover data are available both as maps and as class fractions that are aggregated over each patch. Land-use/land-cover map data are provided in a separate geotiff file for each patch. The naming convention for these files uses the Sentinel-2 patch_id to which we append _esaworldcover.tif. Each file contains a single band with 8-bit integer values that map to land-use/land-cover definitions provided by the ESA WorldCover Product User Manual (https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf) (page 15). The file ben-ge-8k_esaworldcover.csv contains the fractions by which each of the different classes cover the corresponding patch. This product may be useful to generate single-label or multi-label targets for different classification setups. Environmental Data (ERA-5) Weather data are contained in the ben-ge-8k_era-5.csv file. Weather data at the time of observation (temperature at 2 m above the ground, relative humidity, wind vectors at 10 m above the ground) are extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the pressure level at the mean elevation of the observed scene and the time of observation (separately queried for Sentinel-1/2 observations). Environmental data are available in the file ben-ge-8k_era-5.csv. For each patch, identified through the Sentinel-2 patch_id or the corresponding Sentinel-1 patch id patch_id_s1, the file contains the following parameters: * atmpressure_level: atmospheric pressure level at which parameters have been queried [mbar] * temperature_s2: temperature 2m above ground at the time of the Sentinel-2 observation [K] * temperature_s1: temperature 2m above ground at the time of the Sentinel-1 observation [K] * wind-u_s2: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-u_s1: eastward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-1 observation [m/s] * wind-v_s2: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * wind-v_s1: northward component of the wind, at a height of 10 meters above the surface of the Earth at the time of the Sentinel-2 observation [m/s] * relhumidity_s2: relative humidity at the time of the Sentinel-2 observation [%] * relhumidity_s1: relative humidity at the time of the Sentinel-1 observation [%] as extracted from the ERA-5 global reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels) for the patch location. Please see the corresponding documentation for details. Seasonal Encoding To capture the season at the time of observation, we apply a non-linear encoding that scale the date of the observation into the interval [0, 1], referring to [winter, summer] solstice. For any given date, we derive the fractional year and shift it by 9 days such that 21 June has the fractional year 0.5 and 22 December has the fractional year 0 or 1. To account for this ambiguity and the periodicity of the seasons, we modulate the fractional year with a sine function such that 21 June leads to a seasonal encoding of 1 and 22 December leads to a seasonal encoding of 0. Seasonal encodings are provided by the column season in the ben-ge-8k_meta.csv file. Season values cover the interval [0,1] as a continuous variable where 1 refers to summer solstice and 0 refers to winter solstice. Climate zone classification (Beck et al. 2018) Patch-based climate zone classifications, based on the Köppen-Geiger scheme, were extracted from Beck et al. (2018) (https://www.nature.com/articles/sdata2018214), utilizing their present-day 1-km resolution map. Due to geographical focus of BigEarthNet on Europe, only 11 out of 27 different classes are present in this dataset. Please note that patches that are fully covered by surface water have no climate zone class assigned to them (class label equals zero in this case). Labels are encoded as discrete integer values that follow the schema introduced by Beck et al. 2018 in their legend.txt file that is included here: https://doi.org/10.6084/m9.figshare.6396959. Climate zone classification labels are provided by the column climatezone in the ben-ge-8k_meta.csv file. File and directory structure This archive contains the following directory and file structure: | |--- README (this file) |--- ben-ge-8k_meta.csv (ben-ge-8k meta data) |--- ben-ge-8k_era-5.csv (ben-ge-8k environmental data) |--- ben-ge-8k_esaworldcover.csv (patch-wise ben-ge-8k land-use/land-cover data) |--- dem/ (digital elevation model data) | |--- S2A_MSIL2A_20171208T093351_3_82_dem.tif | ... |--- esaworldcover/ (land-use/land-cover data) | |--- S2B_MSIL2A_20170914T93030_26_83_esaworldcover.tif | ... |--- sentinel-1/ (Sentinel-1 SAR data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43/ | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_labels_metadata.json (BigEarthNet label file) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VH.tif (BigEarthNet/Sentinel-1 VH polarization data) | |--- S1A_IW_GRDH_1SDV_20180219T063851_29UPV_70_43_VV.tif (BigEarthNet/Sentinel-1 VV polarization data) | ... |--- sentinel-2/ (Sentinel-2 multispectral data) | |--- S2B_MSIL2A_20170818T112109_31_83/ | |--- S2B_MSIL2A_20170818T112109_31_83_B01.tif (BigEarthNet/Sentinel-2 Band 1 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B02.tif (BigEarthNet/Sentinel-2 Band 2 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B03.tif (BigEarthNet/Sentinel-2 Band 3 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B04.tif (BigEarthNet/Sentinel-2 Band 4 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B05.tif (BigEarthNet/Sentinel-2 Band 5 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B06.tif (BigEarthNet/Sentinel-2 Band 6 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B07.tif (BigEarthNet/Sentinel-2 Band 7 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B08.tif (BigEarthNet/Sentinel-2 Band 8 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B09.tif (BigEarthNet/Sentinel-2 Band 9 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B11.tif (BigEarthNet/Sentinel-2 Band 11 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B12.tif (BigEarthNet/Sentinel-2 Band 12 data) | |--- S2B_MSIL2A_20170818T112109_31_83_B8A.tif (BigEarthNet/Sentinel-2 Band 8A data) | |--- S2B_MSIL2A_20170818T112109_31_83_labels_metadata.json (BigEarthNet label file) | ... |--- splits/ |--- ben-ge-8k_test.csv (index file for test split, 10%) |--- ben-ge-8k_validation.csv (index file for validation split, 10%) |--- ben-ge-8k_train.csv (index file for training splits, 80%) Once unpacked, ben-ge-8k requires 4.2 GB of space. More Information For more information, please refer to https://github.com/HSG-AIML/ben-ge. Citing ben-ge-8k If you use data contained in this archive, please cite the following two papers: M. Mommert, N. Kesseli, J. Hanna, L. Scheibenreif, D. Borth, B. Demir, "ben-ge: Extending BigEarthNet with Geographical and Environmental Data", IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023. G. Sumbul, A. d. Wall, T. Kreuziger, F. Marcelino, H. Costa, P. Benevides, M. Caetano, B. Demir, V. Markl, "BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval", IEEE Geoscience and Remote Sensing Magazine, 2021, doi: 10.1109/MGRS.2021.3089174.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2023 EnglishPublisher:Zenodo Funded by:SNSF | Bioaccumulation and biotr...SNSF| Bioaccumulation and biotransformation of organic xenobiotics in aquatic organismsAuthors: Kiefer, Karin; Müller, Adrian; Singer, Heinz; Hollender, Juliane;Kiefer, Karin; Müller, Adrian; Singer, Heinz; Hollender, Juliane;This is the collection associated with list S60 SWISSPEST19 on the NORMAN Suspect List Exchange. https://www.norman-network.com/nds/SLE/ Swiss pesticides (plant protection products) and metabolites from Kiefer et al 2019 (Eawag), Tables SI-B 1 and 2, DOI: 10.1016/j.watres.2019.114972 Update 25 April 2020: fixed many naming issues in xlsx and csv file. No structural information changed. 25 Mar 2023: fixed date CAS. 25 May 2023: fixed non-live CIDs to live CIDs. 6 Jul 2023: added NOA 413161 structures and transformations file. For full information refer to original publication: DOI: 10.1016/j.watres.2019.114972
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For further information contact us at helpdesk@openaire.eu2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 4Kvisibility views 4,163 download downloads 3,497 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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