Feature Films Database Southern Mediterranean films database Scriptwriters European Film Schools Production Companies Distributors International Sales Submit a Film Industry Reports Co-Production Podcast Online Screenwriting Training Course Guided Course for Feature Film Writing Script Analysis Analysis of the potential of your series Cineuropa's Training Catalogue Film Festival Photographs Newsletter Photogalleries EUFCN Location Award Euro Film Fest 27 Times Cinema GoCritic! Advertise on Cineuropa Logos and Banners by Davide Abbatescianni the first European studio wholly devoted to adult-orientated animation has made the leap from indie to mainstream On day 2 of Marseille’s Cartoon Next (9-11 April), Arthur Delabays, Nicolas Athané and Gabrielle d’Andrimont were invited on stage to present their work for Paris-based Bobbypills the first European studio entirely dedicated to adult-orientated animation The session was moderated by Lisette Looman who worked for too long in the children’s animation industry felt “censored” and trapped by its creative cages and finally ended up yearning to focus on “more mature stories” The studio’s mission is “to give a voice to the talents who will shape tomorrow’s pop culture” adding that it grew rapidly and welcomed a bigger which “helped artists to create the best version of their shows” Delabays said that “courage is mandatory” when it comes “to supporting the artists’ unique points of view on the world” trashy or irrelevant content can exist as long as it comes with very sincere the team always makes sure that “even the dumbest ideas are well executed” Athané spoke about the company’s shift from indie to mainstream we moved on to big international partnerships with Ubisoft The idea [at the core] is still the same for us: stories first Bobbypills involved the whole team in the writers’ room Athané described it as a collective storytelling process similar to that of a workshop but also admitted how unpredictable and hard to replicate it was for bigger productions when working on Warner’s Creature Commandos the team had to stick to the script penned by James Gunn the Bobbypills team divided each episode into different parts and assigned them to four or five storyboard artists splitting the tasks depending on everyone’s unique skills and sensitivities we kept the idea of teamwork but in a more industrial fashion,” Athané summed up d’Andrimont touched upon the studio’s global development strategies and its internal writing process Bobbypills aims to source projects that can “ensure quality and viability” while “writing remains at the basis of everything you can have the best design and animation in the world but it won’t work in the end,” she underscored The studio follows “the typical development timeline” but does something different the team goes up to “the first step of the ladder” the authors and the director focus solely on the concept of the series a dialogue scene and a first animatic in the space of up to one month and a half “This space is essential for us; it’s like a Bobbypills creative lab where ideas are refined and tested to ensure that only the most promising projects go to the next stage.” Delabays revealed that the studio works on three types of projects: AAAs are normally big projects for international streamers ambitious style”; AAs require either “more time and less money” or “more money and less time”; and As are low-budget often short-form series like Vermin and Peepoodo The team also spoke about the making of the second season of Peepoodo billed as “an educational show aiming to explore sexuality in all its glory” in a positive fashion and with no prejudices while delivering a wider “message of tolerance” […] but nobody was bold enough to pay for it We launched a Kickstarter campaign and decided to broadcast it ourselves on our website,” said Delabays The campaign helped the team raise €500,000 gifted by some 8,000 backers from 50 different countries The funds helped the team deliver eight more episodes and made them realise how “connecting with the audience can really make a difference” Bobbypills’ growing slate of productions includes the series Dead Cells the sitcom Down to Earth and the studio’s first feature Please subscribe to our newsletter to receive more stories like this directly in your inbox Series Film Festival Trends European Film Schools Gender Equality, Diversity and Inclusion Europe and the Rest of the World Green Industry Initiatives and Sustainability Distribution, Exhibition and Streaming Market Trends Documentary European Policy all industry reports Subscribe to our newsletter to receive the most important daily or weekly news on European cinema Distribution / Releases / Exhibitors Europe European Arthouse Cinema Day set to return on 23 November Cannes 2025 Marché du Film Indie Sales presents a three-star line-up at Cannes HOFF 2025 The Shadow and U Are the Universe win at Estonia’s Haapsalu Horror and Fantasy Film Festival Crossing Europe 2025 Awards The New Year That Never Came and The Flats crowned at Crossing Europe Cannes 2025 Marché du Film Be For Films to sell Love Me Tender in Cannes Cannes 2025/Sponsored Latvia set to shine bright at Cannes, led by Sergei Loznitsa’s competition entry Two Prosecutors Las Palmas 2025 MECAS/Awards Manuel Muñoz Rivas and Joana Carro win awards at the eighth MECAS Cannes 2025 Marché du Film Playtime to present some high-impact and entrancing trump cards at Cannes Production / Funding Italy Shooting begins on Walter Fasano’s Nino, a portrait of scoring maestro Nino Rota goEast 2025 Review: My Magical World Production / Funding Belgium Wallimage is backing Michaël R Roskam's Le Faux Soir Box Office Slovakia Slovak crime-thriller Černák becomes the highest-grossing film in domestic cinemas Robot T-Oby Giulio CallegariFrench release May 7 Dreams (Sex Love)by Dag Johan HaugerudBerlinale 2025 - Golden Bear for Best FilmGerman release May 8 The Mohicanby Frédéric FarrucciVenice 2024 – Orizzonti ExtraItalian release May 8 Surfacingby Cecilia Atán, Valeria PivatoSan Sebastián 2024 - New DirectorsSpanish release May 9 Jaśmina Wójcik • Director of King Matt the First The Polish director discusses her approach to taking on a 1920s children’s literary classic in an unexpected way Želimir Žilnik • Director of Eighty Plus The Serbian director discusses his deep suspicion of ideologies in relation to his irresistibly charming latest feature, which follows a man whose life spans three political systems Paulina Jaroszewicz • Distribution and marketing manager, New Horizons Association Cineuropa sat down with the Polish distributor to discuss her company’s strategy as well as the connection between its distribution line-up and BNP Paribas New Horizons Festival’s programme Lorcan Finnegan • Director of The Surfer The Irish filmmaker discusses his mystery-thriller, how he created the character with Nicolas Cage and his approach to the use of colours in the film Privacy Policy The images used on this website have been provided by journalists and are believed to be free of rights if you are the owner of an image used on this website and believe that its use infringes on your copyright We will remove the image in question as soon as possible We have made reasonable efforts to ensure that all images used on this website are used legally and in accordance with copyright laws About us | Contact us | Logos and Banners Mission | Partners | Team | Donations | Terms and conditions Metrics details To provide the information needed for a detailed monitoring of crop types across the European Union (EU) we present an advanced 10-metre resolution map for the EU and Ukraine with 19 crop types for 2022 Using Earth Observation (EO) and in-situ data from Eurostat’s Land Use and Coverage Area Frame Survey (LUCAS) 2022 the methodology included 134,684 LUCAS Copernicus polygons Sentinel-1 and Sentinel-2 satellite imagery land surface temperature and a digital elevation model two classification layers were developed using a Random Forest machine learning approach: a primary map and a gap-filling map to address cloud-covered gaps show an overall accuracy of 79.3% for seven major land cover classes and 70.6% for all 19 crop types The trained model was used to derive the 2022 map for Ukraine demonstrating its robustness even in regions without labelled samples for model training employing LUCAS data for training resulted in reduced classification accuracy compared to using solely Corine Land Cover points Further investigation was suggested by the authors The dataset from the “Copernicus module” of the LUCAS 2022 survey encompassing 134,684 polygons across the 27 European Union countries (EU-27) We utilized this data to generate a new 10-meter spatial resolution LULC map for the entire EU-27 focusing particularly on crop type mapping for 2022 and discusses the application of LUCAS 2022 “Copernicus module” data for crop type classification Investigate the optimal set of input features by considering different temporal aggregations of S1 and climate data to obtain a wall-to-wall map with no spatial gaps and the best possible thematic accuracy Study a balanced number of training data to allow the training and application of the classification algorithm for the entire EU-27 while optimizing GEE computing resources Evaluate the classification model’s inference performance in Ukraine where no labelled data was used in the model training stage and validate the results using third-party independent classification data General overview and main steps of the study these polygons were designed to improve accessibility and facilitate data extraction particularly for satellite imagery such as S1 and S2 with a 10-meter spatial resolution This module provides detailed information on LC (66 classes including crop type) and land use (LU) (38 classes) for the mentioned polygons The dataset offers unique opportunities for applications requiring a higher level of thematic resolution This study uses the LUCAS “Copernicus module” 2022 polygons as in-situ field observations to obtain a wall-to-wall LULC map of the EU-27 land. The distribution of these polygons, with the main eight-class scheme, is shown in Figure 2. Distribution of LUCAS 2022 polygons over EU-27 with main land cover categories which includes seven broad categories for Level-1 and 25 detailed categories for Level-2 Multiple EO datasets were used to generate the LULC map for the entire desired area The employed EO data comprises high resolution frequent revisit reflectance data from S2 and backscattering coefficients from S1 including Land Surface Temperature (LST) and Digital Elevation Model (DEM) containing the Digital Surface Model (DSM) and lower resolution LC were utilized The following sections provide a detailed description of each of these categories Harmonized Sentinel-2 MSI: MultiSpectral Instrument The S2-L2A collection contains atmospherically corrected surface reflectance values The “harmonized” aspect of the collection is related to the seamless correction for the reflectance offsets introduced by ESA on 21 January 2022 S2-L2A stores scene classifier information in the so-called Scene Classification (SCL) band S2 temporal series from 1st January to 31st December 2022 were used in this work Only the images with a cloud fraction cover lower than 50% were selected Pixels with a cloud probability higher than 75% and labeled as Saturated or Defective and Snow / Ice in the SCL information were masked It should be noted that thresholds used were determined using a visual To homogenize the spatial resolution of the S2 images with a common spatial resolution the 20 m bands were resampled into 10 m using the nearest neighbour method the median of the first three months was calculated and referred to as the winter median No monthly features were calculated in the spectral field for November or December and 98th percentiles per band and index were also computed a total number of 286 features (26 × (8monthly median + 3yearly percentile)) are available from S2 data for the year 2022 The S1 satellite mission is another dual-sensor constellation deployed under the EU’s Copernicus program with Sentinel-1A (S1A) and Sentinel-1B (S1B) The S1 constellation provides global coverage and a revisit time of 6 days Combining ascending (local evening passes) with descending (local morning passes) acquisitions results in a revisit of more than 6 (12) days though with different incidence configurations all ascending and descending orbits are acquired Although S1 can acquire in different beam modes Copernicus S1 data are full, free, and openly accessible. However, they are provided in Level-1 formats (GRD and SLC), which are not application-ready data formats. S1 Level-1 data needs to be processed to geocode and calibrate backscattering coefficients. In GEE, each GRD scene is preprocessed following a standard recipe scripted in the S1 SNAP Toolbox (http://step.esa.int) and using the SRTM 90 m DEM for geocoding The resulting sigma naught backscatter coefficients (σ0) are made available in the COPERNICUS/S1_GRD_FLOAT collection As microwave data is not affected by cloud coverage issues monthly medians were calculated for all the months a total number of 105 features (7 × (12monthly median + 3yearly percentile)) are available from S1 data this dataset produced representative LST values for every month enabling the assessment of changes in temperature over time at a spatial resolution of 1 km The LST_Day feature containing the monthly average daytime LST for 2022 was used in this study Providing a 30 m grid-spaced terrain model of the surface of the Earth this elevation dataset captures variations in the height and shape of the terrain Considering 12 monthly features for LST data and one for DEM data a total of 13 auxiliary features were used in this study a pan-European inventory that categorizes land cover into 44 thematic classes for the reference year 2018 The CLC dataset provides information on LU and LC across Europe at a spatial resolution of 100 m all auxiliary data was resampled to a spatial resolution of 10 m due to cloud contamination in certain areas a second process was performed to produce the gap-fill map used to fill the gaps in the primary map and obtain the final classification map and additional training samples from the affected areas were added It is worth mentioning that the other parameters were set to their default values the total number of samples resulted in 134,260 with a further reduction to 133,829 after deleting samples containing null values in their LST indicators 29,826 samples were in the Mediterranean region whereas 104,003 samples belonged to the non-Mediterranean region This stratification was done to ensure an equal portion share of train and test data in both zones Based on the LUCAS code in Table 1 (excluding Water the samples in both regions were randomly divided into train and test datasets using a 75% and 25% split ratio the regional train and test datasets were merged pairwise resulting in final train and test datasets with 100,360 and 33,456 samples Thirteen samples were excluded from the process due to an inadequate number of available subclass samples which was insufficient for a proper division Distribution of train and test samples in EU-27 with distinguishing primary and additional gap-fill data to address the imbalanced distribution of samples amongst the different classes in the primary training dataset efforts were made to achieve a more balanced distribution by reducing samples in specific classes and oversampling others The abundance of certain classes was reduced by eliminating similar samples; meanwhile increasing samples for sparse classes was increased by including additional samples from within polygons The produced map underwent a spatial post-processing (applied in GEE) to reduce spatial noise and small patches in the classified map resulting in a smoother and more consistent map The process begins by evaluating the number of interconnected identical pixels between each pixel on the map It is restricted to a maximum of 30 pixels as well as horizontal and vertical orientations (4-connected) and findings are given the name “patch size” A refinement was then applied to pixels with patch sizes smaller than 20 It consists of applying a filtering approach centred around a 10-pixel radius and a square kernel It is important to note that all post-processing values were assigned practically Given the focus of the LUCAS dataset on vegetation cover and a lack of training samples in mountainous (or rocky) or snow-covered areas these areas are mostly classified incorrectly as Artificial land and Water two external layers were utilized for masking pixels identified as Artificial land with elevations exceeding 1000 meters and slopes greater than 10 degrees were masked pixels where their CLC value were 322 (Moors and heathland) 335 (Glaciers and perpetual snow) were masked To ensure the reliability and precision of the EU LULC map a comprehensive evaluation was conducted using both accuracy assessment and spatial analysis methods the classification results were validated against independent reference datasets ensuring that the map accurately represented land cover types the spatial analysis examined the spatial distribution and consistency of classified land cover across different regions a holistic view of the map’s performance is provided that not only verifies overall accuracy but also highlights regional variations Four accuracy assessment and spatial analysis approaches were employed to evaluate the final EU LULC map The final map was evaluated with LUCAS test data as well as additional field samples from the European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) dataset A second validation approach was based on a regional subset of Geospatial Aid Application (GSA) data which is based on the 2022 farmer declaration data aggregated area sums for several main crops obtained from the EU LULC map are compared to the corresponding official Eurostat national statistics The proposed classification workflow was evaluated by the confusion matrix (CM) assessing the 33,456 test samples Five assessment metrics were determined from the CM: User’s Accuracy (UA) F1-score (a weighted average of UA and PA) representing the ratio of correctly predicted samples to the total samples) quantitative measure of reliability the classification) To extract the validation set from EMBAL data, firstly, sensitive and irrelevant attributes to the study, like user ID and geometries of EMBAL parcels, were removed. Secondly, label recoding was performed for certain classes to allow legend matching with LUCAS (Supplementary Table 2) all records from LC classes Arable land (eA) which have a valid lc1 attribute and with an area of more than or equal to 50 square meters were selected In the second approach for the 2022 crop map assessment we expanded our evaluation and compared our classes with vector parcels derived from the Geospatial Aid Application (GSA) across several EU regions The GSA refers to the annual crop declarations made by EU farmers for Common Agricultural Policy (CAP) area-aid support measures Each GSA record represents a polygon of an agricultural parcel with one crop (or a single crop group with the same payment eligibility) providing a valuable dataset for comparison we incorporated GSA data from bewa2022 (Wallonia in Belgium While the GSA system is implemented across the EU the design and operation of each region’s data set vary Each GSA has its own nomenclature for crop types and crop practices eligible for regional support schemes Maintaining a focus on crop classes covering at least 0.5% of the cumulative GSA area in each selected region we ensured that our assessments targeted agriculturally significant areas and crop types we achieved a total area coverage ranging from 89.8% (lowest coverage country) to 97.1% (highest coverage country) of the total parcels for each respective GSA We extracted a total of 2,116,051 parcels spanning over 8.102 million hectares We then compared the declared crops, mapped to the LUCAS legend, to the predicted classes within each GSA parcel from our pixel-based classification. This comparison provided a comprehensive assessment of how practically applicable our classification algorithms are. Table 5 summarizes the result of the GSA data used in the study By comparing the frequency distribution of values in one dataset within classes of another dataset the estimated areas of EU crop maps were compared to the corresponding Eurostat data the Pearson correlation coefficient was calculated in order to assess the strength of the correlation The distribution of these variances is subsequently aggregated to deduce conclusions at an individual crop level incorporating insights from both 2018 and 2022 data in the analysis This study aims to develop the LULC map for the EU-27 area by utilizing training and test data from inside the region the research scope extends beyond this region to include Ukraine no label features were available for the Ukraine territory for use in model training the efficacy and transferability of the classification model trained in one area and validated in a different external region was assessed a better understanding of the reliability of the classification model and its potential broader applicability across external territories will be gained The analysis involved a comprehensive comparison between our generated map and the independently generated high-resolution crop map produced by the Kyiv Polytechnic Institute (KPI) team for the year 2022 (https://ukraine-cropmaps.com) This step was pivotal in understanding any deviations or similarities between the two mapping results providing a robust assessment of our model’s performance against an established standard The dataset is reprojected to the Lambert Azimuthal Equal-Area projection (ETRS89-LAEA EPSG:3035) and it includes individual maps for the EU-27 and Ukraine (GeoTiff) as well as a collection of tiles of 327 km (in width) and 327 km (in height) The tile index is available in ESRI Shapefile format The maps are also available as ImageCollection in the GEE catalogue (https://developers.google.com/earth-engine/datasets/catalog/JRC_D5_EUCROPMAP_V1) which contain required data for EU27 and Ukraine Table 6 presents the number of samples per class and the selected number after the balancing process the F1-score using an RF classifier for the two levels is reported The procedure focused on Woodland and Shrubland and Wetland classes in the Level-1 scheme and the Arable land class in the Level-2 scheme The total number of primary training samples increased from 81,427 to 86,831 after the balancing The impact of the process is noticeable in the results The F1-scores increase for classes that expand in the number of samples no significant change was observed in the F1-scores for the Woodland and Shrubland no modifications were made to the Artificial land samples the OA slightly decreased from 78.9% to 78.4% in Level-1 whereas it increased from 70.9% to 71.1% in Level-2 The κ also showed a marginal change from 0.71 to 0.70 in Level-1 and from 0.62 to 0.63 in Level-2 there was a slight improvement in the Level-2 scheme validation results For analysis purposes, the EU crop map is re-projected to the Lambert azimuthal equal-area (ETRS89-LAEA, EPSG:3035) projection. The final LULC map presented in Figure 4 comprehensively depicts 25 distinct classes, encompassing around 3.973 million square kilometres (Mkm2) of land area. This value was calculated after performing the masking process in post-classification. EU-27 Land Use and Land Cover map at 10 m pixel size for the year 2022. The letters refer to zoom views in Figure 5 The resulting map was visually inspected and spatially consistent with high spatial resolution base maps and existing products such as the EU crop map 2018, particularly in terms of the major LC categories. For each class, a simple area estimation based on pixel counting is presented in Supplementary Table 4 Figure 5 illustrates parcels of varying sizes and crop types across different regions of EU-27. These classifications were achieved without prior knowledge, relying solely on non-parametric classification methods. Parcel boundaries are clearly distinguishable even when using pixel-based classification methods. EU-27 Land Use and Land Cover map for a subset of regions in (a) Castile and León (Spain), (b) Centre-Val de Loire (France), (c) Lower Austria (Austria), (d) Sud-Muntenia (Romania). Legend is presented in Figure 4 Based on the evaluation of 32,628 out of 33,456 test samples (828 samples were unavailable due to the masking process), a confusion matrix was generated to assess the classification accuracy of seven major LC classes (Table 7) demonstrated excellent F1-scores ranging from 73% to 87% These high scores indicate a robust capability to accurately classify these specific LC classes and Wetlands classes proved to be more challenging conceivably due to the limited number of training samples available for these classes Nineteen test samples could not be evaluated due to possible small remaining gaps in the final LULC map indicating a reasonably high level of correctness in the classification results indicating substantial agreement beyond chance This suggests challenges in distinguishing these LC classes from others The remaining 11 classes showed F1-scores ranging between 30% and 69% The classification accuracy of available 33,308 out of 34,370 EMBAL validation samples containing 18 LC classes was evaluated. Notably, the number of 1062 samples were located in masked areas. A confusion matrix (Table 8) was calculated An alternative evaluation was conducted utilizing an independent data source: farmers’ declarations parcel information and their associated crop types were derived from these declarations for crop classes representing at least 0.5% of the total area in the GSA The selected features were then transformed to align with the grid of the EU crop map and the GSA crop classes were translated into the corresponding EU LULC codes Confusion matrices were derived for each region Producer’s accuracies comparing GSA farmer declaration data for EU LULC crop codes and specific country/region Rape and turnip rape is notable with a PA of 97.7% vegetables and flowers” have moderate accuracies ranging from 60.1% to 66.2% pointing to potential misalignments with the actual data Barley also shows a commendable PA of 89.7% The Netherlands region nl22 exhibits high accuracies for Potatoes vegetables and flowers” category shows a lower accuracy signalling an area where the crop map might be refined and Sugar beet has a slightly higher accuracy suggesting some level of misclassification indicating significant discrepancies from the independent data The EU crop map 2022 displays strong classification accuracies for crops like Sugar beet indicative of a reliable algorithm for these crops hinting at potential issues with spectral overlap The observed regional variability in accuracy also suggests that localized conditions affect classification success pointing to a need for region-specific algorithm adjustments and improved data handling to enhance overall mapping precision The areas reported by Eurostat at country level are compared with the area retrieved from the EU 2018 and EU 2022 crop maps and R2 is the coefficient of determination Ukraine’s Land Use and Land Cover map at 10 m pixel size for the year 2022 Confusion between wheat and barley is considerable The LULC classification produced in this study is based on monthly median feature values and it presents some considerable limitations The primary constraint of the input features used is its temporal resolution Despite their ability to provide a broad overview of LC dynamics monthly medians are unable to capture finer temporal nuances such as seasonal transitions or short-term disturbances may not be adequately reflected or may even be overlooked altogether in crops with similar growing behaviour This problem is more accentuated when mapping areas that span over a great gradient of longitudes and latitudes Variations in different areas can lead to substantial seasonal variations that affect growing patterns and Latitude significantly impacts crop development patterns as the angle and intensity of sunlight depend on it Areas at lower latitudes receive intense sunlight throughout the year compared to regions at higher latitudes Due to the availability of ample energy for photosynthesis crops exhibit a relatively stable and higher NDVI which indicates productive and healthy vegetation southern regions also exhibit more frequent and variable water stress and drought conditions that limit crop growth and influence phenological patterns NDVI trends for the 2022 crop growth of three different crops: Common Wheat and Rapeseed (Rape and Turnip Rape) in Slovakia and Spain As depicted in Figure 9 which results in crops reaching their peak growth earlier than in SK the growth behaviour of the three mentioned crops exhibits striking similarities in both regions Durum Wheat starts the season and attains its peak growth earlier while Common Wheat and Rapeseed follow a slightly delayed growth trajectory can impact the local climate and vegetation patterns and precipitation patterns may differ from those in inland regions at the same latitude resulting in differences in crop NDVI based on these factors NDVI trends for the 2022 crop growth of Common Wheat LULC classification at the continent scale with different climates based on a single classifier and based on monthly median input features It is important to recognize that the growth behaviour of the same crop types can exhibit shifts influenced by regional variations as well as crops with similar growth patterns It is often difficult to capture these subtle differences using only monthly medians The use of more advanced time series analysis or deep learning techniques by integrating all available acquisitions as well as utilizing more suitable auxiliary data is recommended to overcome these limitations the EU Crop Map 2022 provides a refined lens for European agricultural and environmental analyses These maps make the interplay between crop diversity and agricultural system resilience more tangible empowering stakeholders to strategize against potential disruptions across various scales The map can be used as an input to account for the environmental implications of pesticide use Highlighting regions close to sensitive and urban areas underscores the need to balance agricultural output with ecological preservation The map also provides an essential base-layer needed to evaluate the potential effects of agricultural intensification on biodiversity such as the ongoing war between Russia and Ukraine this resource proves invaluable in assessing agricultural impacts guiding both national and European strategic responses the EU Crop Map 2022 has the potential to enhance agricultural research and to play a pivotal role in shaping responsive and sustainable policies across the continent These new European high-resolution and satellite-sourced maps provide an unprecedented view into the continent’s agrarian landscape Through their European-wide coverage and detail they can underpin higher precision agronomic they have the potential to reshape policy-making approaches and fortify the EU’s commitment to sustainable food production along with guidelines contained in a README file A survey of image classification methods and techniques for improving classification performance Remote sensing for biodiversity science and conservation Hyperspectral remote sensing of vegetation 2019: Climate Change and Land: an IPCC special report on climate change and greenhouse gas fluxes in terrestrial ecosystems (2019) Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments International Journal of Applied Earth Observation and Geoinformation 26 Remote Sensing Methods for Flood Prediction: A Review Applications of Remote Sensing in Precision Agriculture: A Review Crop Yield Prediction Using Multi Sensors Remote Sensing (Review Article) The Egyptian Journal of Remote Sensing and Space Science 25 Commercial Off-the-Shelf Digital Cameras on Unmanned Aerial Vehicles for Multitemporal Monitoring of Vegetation Reflectance and NDVI Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices Optimizing land use decision-making to sustain Brazilian agricultural profits Combining Crop Models and Remote Sensing for Yield Prediction: Concepts Applications and Challenges for Heterogeneous Smallholder Environments A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling International Journal of Applied Earth Observation and Geoinformation 9 Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century Analysis and Modeling of Agricultural Land use using Remote Sensing and Geographic Information System: a Review (2013) An Overview of Platforms for Big Earth Observation Data Management and Analysis a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery Towards operational validation of annual global land cover maps Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10) Global land use / land cover with Sentinel 2 and deep learning 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (2021) An automated classification of Landsat Thematic Mapper data Photogrammetric engineering and remote sensing Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data International Journal of Remote Sensing 34 A review of large area monitoring of land cover change using Landsat data Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping Global land cover mapping from MODIS: algorithms and early results Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products A time series of land cover maps of South Asia from 2001 to 2015 generated using AVHRR GIMMS NDVI3g data Fourier analysis of multi-temporal AVHRR data applied to a land cover classification International Journal of Remote Sensing 15 Spectral matching techniques to determine historical land use/Land cover (LULC) and irrigated areas using time-series 0.1 degree AVHRR Pathfinder Datasets Google Earth Engine: Planetary-scale geospatial analysis for everyone 2015 Land cover map of Southeast Asia at 250 m spatial resolution Automated cropland mapping of continental Africa using Google Earth Engine cloud computing ISPRS Journal of Photogrammetry and Remote Sensing 126 Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples ISPRS Journal of Photogrammetry and Remote Sensing 167 Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the A semi-automated approach for the generation of a new land use and land cover product for Germany based on Landsat time-series and Lucas in-situ data Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data International Journal of Applied Earth Observation and Geoinformation 88 Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS European Commission, Joint Research Centre (JRC). LUCAS Copernicus 2022. European Commission, Joint Research Centre (JRC) https://data.jrc.ec.europa.eu/dataset/e3fe3cd0-44db-470e-8769-172a8b9e8874 (2023) d’Andrimont, R. et al. Advancements in LUCAS Copernicus 2022: Enhancing Earth Observation with Comprehensive In-Situ Data on EU Land Cover and Use, Earth Syst. Sci. Data Discuss [preprint], https://doi.org/10.5194/essd-2023-494 (2024) Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services Airborne multispectral data for quantifying leaf area index and photosynthetic efficiency in agriculture Development of a two-band enhanced vegetation index without a blue band Use of a green channel in remote sensing of global vegetation from EOS-MODIS Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) Preliminary User Feedback on Sentinel-2A Data In Proceedings of the Sentinel-2A Expert Users Technical Meeting Preprocessing Transformations and Their Effects on Multispectral Recognition Using thematic mapper data to identify contrasting soil plains and tillage practices Photogrammetric engineering and remote sensing 63 A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery Use of normalized difference built-up index in automatically mapping urban areas from TM imagery International Journal of Remote Sensing 24 Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery International Journal of Remote Sensing 27 The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features International Journal of Remote Sensing 17 Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing Comparison of Filters Dedicated to Speckle Suppression in Sar Images A General Characterization for Polarimetric Scattering From Vegetation Canopies Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park Gabon: overcoming problems of high biomass and persistent cloud Vegetation cover monitoring in tropical regions using SAR-C dual-polarization index: seasonal and spatial influences International Journal of Remote Sensing 42 MODIS/Terra Land Surface Temperature/3-Band Emissivity Monthly L3 Global 0.05Deg CMG V061 NASA EOSDIS Land Processes Distributed Active Archive Center (2021) NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team Precise Global DEM Generation by ALOS PRISM European Environment Agency. CORINE Land Cover 2018 (raster 100 m), Europe, 6-yearly - version 2020_20u1, May 2020. European Environment Agency https://land.copernicus.eu/en/products/corine-land-cover/clc2018 (2019) Decision Forests: A Unified Framework for Classification Manifold Learning and Semi-Supervised Learning European Environment Agency. Biogeographical regions in Europe. European Environment Agency https://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2 European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) Sensing‐New Insights into Grassland Science and Practice European Commission, Joint Research Centre (JRC). EUCROPMAP 2022. European Commission, Joint Research Centre (JRC) https://doi.org/10.2905/555e5d1d-1aae-4320-a716-2e6d18aa1e7c (2022) JRC-BDAP. VOIS library. Code development platform for open source projects from the European Union institutions https://code.europa.eu/jrc-bdap/vois (2023) Machefer, M. et al. Earth Observation based multi-scale analysis of crop diversity in the European Union: First insights for agro-environmental policies. Agriculture, Ecosystems & Environment, 374, 109143, https://doi.org/10.1016/j.agee.2024.109143 (2024) Good practices for estimating area and assessing accuracy of land change Ghassemi, B., Izquierdo-Verdiguier, E. & Vuolo, F. Google Earth Engine Scripts for Land Use Land Cover (LULC) Classification of the European Union (EU27). Zenodo https://zenodo.org/doi/10.5281/zenodo.10220964 (2023) Download references This project was co-funded by the European Union’s Expert Contract No CT-EX2023D752321-101 under the EU Crop Map 2022 – a 10-m crop type map of EU based on the Earth Observation satellite and LUCAS Copernicus 2022 This research was also co-funded by the European Union’s Horizon Europe Research and Innovation Programme under Grant Agreement No 101060423 LAMASUS project University of Natural Resources and Life Sciences Emma Izquierdo-Verdiguier & Francesco Vuolo Marijn van der Velde & Raphaël d’Andrimont Davide De Marchi: Develop the dashboard viewer The authors declare no competing interests Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Download citation DOI: https://doi.org/10.1038/s41597-024-03884-y Anyone you share the following link with will be able to read this content: a shareable link is not currently available for this article Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research By Kévin Giraud | 10/10/2024 5:37 pm | Be the First to Comment! French Tiktok and Instagram sensation The Broos (Les Gros in French) pitched a longer-form version of the project at Cartoon Forum last month and directed by Broos creator David Mirailles and Bobby Prod narrative director Youssef El Kouch Adapted from the eponymous comics, The Broos turned into a Tiktok hit with nearly six hundred thousand followers Using a sketchy black-and-white style and witty dialogue the series captures the everyday adventures of two average French guys From couch hangouts to car trips and parties the series follows their evolving friendship depicting a kind of bromance which is not commonly portrayed in French fiction where another Arte-funded series Samuel gathered more than seven million views earlier this year (out of 25 million views across all platforms) this type of content is definitely trending allowing the Broos creators to project themselves into further development of this heartfelt story Ahead of their pitch at Cartoon Forum last month Cartoon Brew spoke with Mirailles and Bobby Prod development manager Gabrielle d’Andrimont about how they intend to bring this social media sensation to a broader audience while retaining the elements that made the show such a success on social media Cartoon Brew: How did you approach the adaptation of Broos into an animated series David Mirailles: It happened quite naturally because I had been wanting to experiment with longer formats for a long time I definitely wanted to expand the stories and go more in depth of the characters episodes were supposed to be eleven minutes long then we settled for the four-and-a-half minute format which allows me and Youssef to develop the story ♬ son original – Les Groos What is the place of fiction on platforms such as Tiktok as of today Mirailles: I think there’s a lot of vacant space to be filled there was very little fiction shared on Tiktok and even now there’s still a lot more that could be done and I think animated fiction clearly has its place on this platform and users now expect more quality content from creators and now we want to expand that to an even bigger audience That’s why we will continue this project with a double narrative with a 12 x one-minute series published only on social media to develop a cross-media approach How does the Broos project fit into Bobby Prod’s editorial line Gabrielle d’Andrimont: It’s very different from anything we’ve done until now Broos is a much more tender kind of content than what we usually produce it still has what you can find in every Bobby Prod series a tone which is also quite radical in its execution and artistic direction which is assumed with a deep down sincerity And that’s exactly the kind of values we are looking for at Bobby what type of broadcasters or partners are you looking for we’ve finished the development phase and already have an agreement with Arte as producer and main broadcaster We are therefore looking for the right partners that will allow us to go into production Our main goal now is to attract international broadcasters or an international sales agent/distributor who can add its skillset to the project Even though the project is mainly aimed at a French-speaking audience Broos universal story and touching characters have the power to go beyond this series has international potential and could reach a global audience of young adults who’ll relate to this modern bromance Could you elaborate on this particular ‘bromance’ that’s taking place between the main protagonists it was really important to depict this type of relationship between two mal characters The Broos idea actually came to me after watching two young men that were on a school trip together but as soon as nobody was watching their relationship changed altogether They were in fact very supportive of each other I remember saying to myself : “Even though I’m very close to my male friends some of whom I’ve known since kindergarten we rarely express such love and affection to each other.” I think it’s really important today that this becomes a new normality and that everyone can benefit from healthy male friendships I hope we can share this feeling with our series Kévin Giraud is a journalist and animation buff based who has been writing as a freelancer in French and English for half a decade He is also the happy father of four: three kids and one Belgian cinema magazine The dates displayed for an article provide information on when various publication milestones were reached at the journal that has published the article activities on preceding journals at which the article was previously under consideration are not shown (for instance submission All content on this site: Copyright © 2025 Elsevier B.V. The Annecy International Animation Film Festival recently concluded with the triumphant French feature film therefore do we explain the French animation sector's current vitality and international appeal Erika Forzy and Gabrielle d'Andrimont provide some of the answers In the latest work by American-Russian director Genndy Tartakovsky a neanderthal and a tyrannosaurus fight for survival in Primal a bloody prehistoric fantasy series broadcast on Adult Swim two sisters battle each other with the help of magic and technology in the Netflix series Arcane which is adapted from the video game League of Legends a 12-year-old future villain wants to conquer the world with the help of his little yellow assistants in the blockbuster Minions: The Rise of Gru from the Franco-American studio Illumination Mac Guff is trying to find a place to live in peace in the animated documentary Flee which won awards at Sundance and Annecy in 2021 What do all these animation projects have in common They were made - in whole or in part - by French studios Their importance in the global animation landscape is due as much to their technical know-how as to their structural assets as major sector players: Charlotte de La Gournerie co-founder of the Franco-Danish studio Sun Creature (Flee) and the producers of the studio La Cachette (Primal) Erika Forzy and Gabrielle d'Andrimont explain: the number of animated foreign film projects made in France that have benefited from the TRIP (Tax Rebate for International Production) has more than doubled (from 8 in 2015 to 18 in 2021) This special scheme offered by the CNC is a major asset for foreign productions and can amount to 30% of the total budget more than 260 million euros in spending pledges were recorded The Netflix series Arcane is a symbol of this success and has become a true phenomenon in its own right - produced by the studio Fortiche Production in Paris and Montpellier two episodes of the Marvel animated series What if... (Disney+) have also been produced in France American orders now represent the majority of green-lit projects the studio La Cachette has also produced an episode of Netflix science fiction anthology series Love Gabrielle d'Andrimont explains: "The director and one of La Cachette's founding partners put a selection of small teasers online which then went viral and Genndy Tartakovsky and Tim Miller who are very switched on and watch everything When we initially met in Annecy it was very informal Death & Robots we were recommended by Owen Sullivan the relationship with Genndy Tartakovsky (Samurai Jack As Erika Forzy explains: "Genndy gets his hands dirty which is quite rare for this kind of project Usually directors are also executive producers - they make pages and pages of notes hold Zoom meetings...With Genndy we have a close and very collaborative relationship We do everything from directing to delivery The studio Sun Creature is preparing a series adapted from the video game Splinter Cell for Netflix and has already created kinematics for Legends of Runeterra - a game derived from League of Legends: "With a franchise of this calibre you don't have to look far for money: Riot Games are financing Legends of Runeterra and Netflix are funding Splinter Cell...These are very big budgets We were able to work with Japanese animators on Legends of Runeterra even though it is usually very difficult to get access to them Gabrielle d'Andrimont sums it up: "La Cachette has a very particular style: a mixture of Japanese and Disney animation which merges East and West," so-called after the renowned Parisian animation school of the same name a network of French animation film schools lists no fewer than 72 animation film studios in France and 30 schools training in the sector This dense network allows for a real variety of talents to emerge mainly attached to "traditional" two-dimensional animation A technique that is highly sought after by the Americans according to Charlotte de La Gournerie: "In the United States Coca-Cola commissioned us to do a 2D commercial for the Super Bowl but shot in Denmark We also did some Miyazaki-style spots for the State of Oregon The soda lobby decided not to show our ad because it was just after Trump's election and they were afraid of ruffling some feathers...In the end but it has come back with a vengeance in recent years Only in France do we have studios capable of producing traditional The producer also highlights a desire among foreign audiences for adult animation which is a proving more difficult to set up in France "Our international clients have a real taste for adult animation and they are not afraid to get stuck in In France there is a real desire to do the same but it is harder to get beyond the trial phase with broadcasters As a result it is more difficult to make projects with greater graphic and narrative ambition allowing for a variety of stories to be told Sun Creature studio used to be based exclusively in Denmark but in January 2021 opened up a branch in Bordeaux "We set up Sun Creature France in order to access the tax rebate as Denmark is one of the few countries that does not have one," explains Charlotte de la Gournerie "In Bordeaux we produced a series of forty eleven-minute episodes: The Heroic Quest of the Valiant Prince Ivandoe This was Cartoon Network's first project outside of London and they wanted a studio that could access this type of crucial funding being based in Paris is mainly to do with image: "Our studio is located in the 14th arrondissement We discovered during the pandemic that many people like to work remotely you get to learn more and there is lots of interaction..." “I'm not sure that being located in Paris changes anything though," says Gabrielle d'Andrimont There are animation centres all over France now: for example in Nouvelle Aquitaine Whilst there are big animation companies in Paris they are creating branches in other regions - in particular to attract regional funds which in turn contributes to the vitality of the animation sector throughout the country in Paris we are able to capture young talents fresh from school who want to grow and evolve with us Being in Paris is also useful for foreign investors who happen to be in town but on the whole all you need is a computer in order to work as animation can be done from anywhere being together allows for a better synergy Lots of projects are being set up and a lot of beginners are arriving on the market while veterans of the animation sector are still on the scene It is true that there is a "gap" between the two generations and I would say that there is a lack of senior players and mentorship we are lucky in France because it's even harder abroad" While there is no risk of a drop in demand the situation is becoming more problematic in terms of supply Charlotte de La Gournerie analyses the talent crisis as being “very difficult to recruit French talent because of The platforms have increasingly large budgets and therefore pay a lot more than TV channels There exists a real inflation of artists' salaries and it is problematic - not only in France but in the UK too where Netflix has caused a big increase in salaries According to the co-founder of Sun Creature the quality of French animation is inseparable from its European dimension: "The meeting of cultures is very interesting as young European talents want to work together; for example on discovering the folklore of Eastern European countries and offering a diversity in narrative and technical approaches We will also have to tell the story of what is happening in Ukraine with Ukrainian talent In order to offer something to the platforms we need to be structured and we need to have weight The JRC has published the first continental map of crops grown in the European Union at 10-m resolution This map combines Copernicus Sentinel-1 satellite observations and in-situ LUCAS 2018 Copernicus data using machine learning and cloud computing For the first time we have a map that allows us to zoom down to cultivated parcels for the entire European Union (EU) territory The EU crop map covers 91 million hectares of cropland and consists of more than 9 billion 10-m pixels The map is based on Eurostat LUCAS in-situ data and Sentinel-1 (S1) Synthetic Aperture Radar (SAR) observations JRC scientists used machine learning and cloud computing infrastructures to bring these datasets together and develop accurate mapping algorithms Algorithms trained on time series of S1 data from the main growing season (January to end of July) capture the crop growing during that period on any agricultural area and other types of crops (19 types in total) is mapped for the first time at a very fine spatial scale Published in Remote Sensing of the Environment this research opens new avenues for agriculture and biodiversity monitoring from the parcel all the way up to the European continent The Copernicus S1 is a two-satellite (Sentinel-1A and Sentinel-1B) constellation carrying a C-SAR sensor which operates with its own source of microwave radiation Since radar ‘looks’ through the clouds it operates in all weather conditions and all of the S1 sensor’s data acquisitions can be used for crop-type mapping Although dependent on year-to-year phenological development findings from the JRC study regarding the best timing for mapping specific crop types are likely to be valid for future studies and can underpin future operational services The Land Use/Cover Area frame Survey (LUCAS) is an evenly spaced in-situ land cover and land use ground survey exercise that extends over the whole of the EU A new LUCAS module specifically tailored to Earth observation (EO) was introduced in 2018: the LUCAS Copernicus module This module surveys the land cover extent up to 51 m in four cardinal directions around a point of observation offering in-situ data compatible with the spatial resolution of high-resolution sensors such as Sentinel-1 and Sentinel-2 Since it provides such useful information for EO applications the Copernicus module will be repeated in 2022 for 150,000 samples in the EU The Copernicus fleet of Sentinel satellites are transforming how policy impacts can be monitored from space By providing synergistic and 10-m scale near-daily revisit data over Europe the Sentinel-1 (used in this study) and the Sentinel-2 constellation (which carries optical sensors) have pushed land monitoring into the big data era Arable land represents almost half of the EU’s land surface area and the common agricultural policy (CAP) accounts for 38% of the EU budget Monitoring arable land across scales is therefore crucial for implementing and evaluating EU-wide environment which has an accuracy rate of about 80% (as verified by comparing with LUCAS data and farmers’ crop declarations) is freely available for anyone to access and use it The EU crop map is a product of the current momentum towards combining Copernicus Sentinel fleet observations extensive in-situ data and cloud computing More scientific and methodological development is needed to transform this first demonstration into an operational in-season mapping service and to translate spatial information into sound environmental indicators Such perspectives also rely on the availability of high-quality (in-situ and satellite-based) European data (such as those provided by Copernicus) and ad-hoc European cloud computing infrastructure From parcel to continental scale – A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations LUCAS Copernicus 2018 : Earth-observation-relevant in situ data on land cover and use throughout the European Union Accuracy Assessment of the First EU-Wide Crop Type Map with Lucas Data Copernicus – Europe’s eyes on Earth LUCAS – Land use and land cover survey Metrics details Accurately characterizing land surface changes with Earth Observation requires geo-located ground truth a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS) A total of 1351293 observations at 651780 unique locations for 106 variables along with 5.4 million photos were collected during five LUCAS surveys these data have never been harmonised into one database limiting full exploitation of the information This paper describes the LUCAS point sampling/surveying methodology including collection of standard variables such as land cover and full resolution landscape and point photos and then describes the harmonisation process The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU The database is valuable for geo-spatial and statistical analysis of land use and land cover change its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12841202 has remained comparable for all five surveys The in-situ nature of the survey implies that the majority of the data are gathered through direct observations made by surveyors on the ground Those points which are unlikely to change and points which are too difficult to access are classified by photo-interpretation in the office using the latest available ortho-photos or Very High Resolution (VHR) images Although most of the points a-priori assigned for in-situ assessment can effectively be visited in the field because of lack of access to the point or the point location being at more than 30 minutes walking distance from the closest point reachable by car Those points are thus photo-interpreted on ortho-photos or Very High Resolution (VHR) images in the field by the field surveyor sometimes a significant difference exists between the theoretical LUCAS point and the actual GPS location reached by the surveyor Observations are collected for the LUCAS point while the photos are taken at the actual GPS location Both locations and the distance between them is noted down Several drawbacks become apparent when working with the original LUCAS datasets While the inconsistencies could be due to the enumerators’ subjectivity in interpretation of the legends and the legend itself it is also related to the complexity of the field survey: large number of surveyors (>700) complex documentation for the enumerators (>400 pages combining all the documents) These drawbacks hinder the further use of the LUCAS data by the scientific community as a whole and in particular by users who are active in emerging fields of big data analytics Inconsistencies and errors between legends and labels from one LUCAS survey to the next which is hampering temporal analysis Missing internal cross-references in the datasets that would facilitate computation and linking observed variables The original full resolution photos taken at each surveyed point are not available for download The lack of a single-entry point or consolidated database hampering automated processing and big data analysis we have gone through an extensive process of cleaning by semantic and topological harmonisation along with connecting the originally disjoint LUCAS datasets in one consolidated database with hard-coded links to the full-resolution photos The LUCAS primary data includes alpha-numerical variables and field photographs linked to the geo-referenced points which contains information on variable name data type and description in a more consolidated fashion making it easier to find information about the relevant variable The third and final step in Protocol 1 is the generation of the mapping files used for value recoding The workflow maps the ascertained relationship between those variables that are the same but have changed in name or alpha-coding between surveys To recode all variables coherently from one survey to the next All transformations are done by recoding ordinal variables to be compliant with the encoding of variables used in the last survey (2018) These mappings serve as a blueprint for the transformation and data integration described in Protocol 2 The harmonisation workflow, alongside the performed database consistency checks, is shown in Fig. 2 and the code is described in code section (section Code availability). The general principle of the harmonisation workflow was to convert all the field legends to fit with the latest i.e. the 2018 database layout (the next LUCAS is planned for 2022). Processing workflow to harmonise the survey data Asterix used to indicate steps after which there is a performed consistency check (Merge into single table Rename columns - iteratively renaming columns to align them with the last (in this case 2018) survey Performed on all tables but 2018 by using the Rename_cols() function from the package Add photo column 2006 - adds columns photo_north, photo_south, photo_east, photo_west, and photo_point on account of them missing from the 2006 base data. Adding is done by cross-referencing the EXIF picture database (see section Overview of EXIF photos database) Performed solely on table for 2006 by using the Add_photo_field_2006() function Add Missing columns - iteratively adding all columns that are present in one table and not present in the others Performed on all tables by using the Add_missing_cols() function Add new columns - iteratively adding all newly created columns. These include the variables ‘letter group’, ‘year’, and ‘file_path_gisco_n/s/e/w/p’ (for more information check Online-only Table 3) Performed on all tables using the Add_new_cols() function Upper case - iteratively converting all characters of selected fields to upper case Performed on all tables using the Upper_case() function Re-code variable - iteratively re-coding selected variables according to created mapping CSV files designed referring back to the reference documents Performed on all tables but 2018 by using the Recode_vars() function Order columns - iteratively ordering all columns according to the template from the 2018 survey Performed on all tables but 2018 by using the Order_cols() function The third part of the harmonisation process includes the merging of the harmonised tables of each survey year plus additional steps listed below before exporting the final data outputs Merge into single table - Merge the five harmonised tables to one unique table via Merge_harmo() function Consistency check performed after this successful execution on newly generated Table Correct theoretical location - Applying a correction of the values of columns th_long and th_lat for merged harmonised table according to the latest LUCAS grid via the Correct_th_loc() function Add geometry columns - Location of theoretical point(th_geom) lucas transect geometries (trans_geom) and distance between theoretical and survey point (th_gps_dist) and spatial index via the Create_tags() function Add number of visits column - column to show the number of times between the years when the point was visited thanks to the Add_num_visits() function or spelling difference in the user-created mapping CSVs used to generate labels in subsequent function that converts encoding to label by aligning them to the mapping CSV of the latest survey Consistency check performed after this successful execution on newly generated mapping CSVs Convert encoding to label - Create columns with labels for coded variables and decodes all variables where possible to explicit labels Consistency check performed after this successful execution Final column order - Re-order columns of final tables with the Final_order_cols() function Remove variables - optional function to remove variables which the technician deems not necessary for the new harmonised product Update record descriptor - Updates Record descriptor by adding a field (year) showing the year for which the variable exists and removing variables listed in the optional function for removing variables from record descriptor The table is exported as CSV and the geometries as shapefiles The full workflow is dependent on two software prerequisites an installation of R (more about the versions used in section Code availability) The pipeline is provided as a R package for ease of reproducibility and transparency (section Code availability) The hard-coded HTTPS links to each photo in the consolidated database allow for large data volume queries and selection tasks This record descriptor specifies variable name In the documentation one can find more information about the variable and a short description along with comments concerning the variable that the authors have deemed important the tables in LUCAS-Variable and Classification Changes contain documentation for users to quickly identify the differences between LUCAS campaigns and the harmonised database “References”: Description and a legend of the used colors of the different tables; “Harmonised DB”: a comparison of all the collected variables of the 2018 survey with the variables of the harmonised database and an overview of the actions to harmonise the data; “Variable changes”: an overview/ comparison of all collected variables between all campaigns from 2009 to 2018 highlighting the changes; “LC (LU) changes”: an overview of the possible LC and LU codes of each campaign highlighting the changes LUCAS transect lines (250-m east looking lines) 1 031 813 observations (76.36%) were done in-situ 94% have been surveyed within 100 m distance of the theoretical LUCAS point and 6% were more than 100 m away from the point The proportion of points where actual in-situ data was collected has decreased from 92.18% in 2006 to 63.67% in 2018 147574) that were visited in-situ turned out not be accessible in practice and are photo-interpreted in the field The number of points surveyed per country and per year ranged between 79 (Malta) to 48215 (France) 1677 points were out of national territory “NOT EU” corresponding to water outside national borders or countries including Russia Distribution of land cover classes in the multi-year harmonised LUCAS database In cases where survey years are not present please orientate oneself with reference to adjacent classes of the same color Counting for the distribution of each class begins at 2018 and ends with 2006 due to the relative abundance of 2018 in terms of classes compared to other years representing subcategories of these main classes are indicated by a combination of the letter group and further digits Number of visits to each LUCAS survey point over the five surveys between 2006 and 2018 the photos with geo-location information have increased considerably through time there is no information on orientation for the photos taken in 2006 and 2009 and 6.7% of the photos have EXIF orientation information for 2012 Overview of the data available for a LUCAS point that was visited five times: (a) Point (c) Zoom showing the point (3-m diameter in green (d) Visit frequency on a 20 by 20 km square centered on the point and (e) In-situ land cover observation of the point for the different years It was decided that having this information in a separate table is more sensible in terms of storage size and accessibility whereby cross-table checks can easily be performed by executing joins between the tables based on point ID and year of survey By combining this information from the two tables (i.e the multi-year harmonised LUCAS survey database and the EXIF table database) one arrives at a significantly large set of labeled examples corresponding to images of the 77 different types of recorded land cover.The background RGB imagery for (c) and (d) is obtained from “Map data ©2019 Google” The first part of this section briefly summarises the LUCAS field surveys quality check The section then focuses on analyses carried out specifically to support the technical quality of the multi-year harmonised LUCAS database process an automated quality check verifies the completeness and consistency after field collection all surveyed points are checked visually at the offices responsible for collection an independent quality controller interactively checks 33% of the points for accuracy and compliance against pre-defined quality requirements including the first 20% observations for each surveyor to prevent systematic errors during the early collection phase The presented data consolidation effort seeks to enhance the quality of an existing product. Ensuring data quality by harmonisation throughout the years is thus essential. Data quality was ensured by taking into account validity, accuracy, completeness, consistency, and uniformity throughout data processing (Fig. 2): Validity of the harmonised database was ensured via data type (for which information can be found in the record descriptor) and a unique constraint of a composite key (consisting of the point ID and year of survey) Accuracy of the data relies on the source data for which the quality was assessed as described in the previous paragraphs Completeness checking shows that since several variables have been added over the years Consistency across surveys has been enhanced All surveys were harmonised towards the 2018 survey Consistency of the presented dataset was internally ensured through running checks at various stages of processing Uniformity checks revealed that the geographical coordinates in columns th_long and th_lat show different locations between some survey years it was decided to have the values of these variables hard coded from the LUCAS grid Because the LUCAS grid is a non-changing feature of all LUCAS surveys the location of each point remains the same throughout the years Thus any discrepancy between the recorded theoretical location of a LUCAS point in the micro data and the grid must be corrected This was done for all but 64 points from 2006 which where recorded on an inaccurate location and were thus removed from the grid Comparison of distributions between (a) calculated distances and (b) surveyed distances between LUCAS theoretical points and actual GPS position of surveyor. The red-colored part of the distribution in subfigure (b) represents the data from 2006, which is copied from the calculated distances (th_gps_dist). Stability of points and location change over time as illustrated: (a) Example of a surveyed point (id 40402278) at close distance (<2 m) and (b) Example of a surveyed point (id 63861648) at large distance (1938m) Location change can be either because of survey conditions The background RGB imagery is obtained from “Map data ©2019 Google” In addition to the theoretical grid and survey point location this data descriptor provides the East-facing transect geo-location data No additional geo-located spatial information is collected in the transect module and this is probably a shortcoming in the survey design resulting from trade-offs between the cost of the survey and its objectives The theoretical transect line (with the same geometry as the one provided with this data descriptor) is displayed on the ground document of the surveyor The surveyor has then to walk on the line and to record the successive land cover and landscape elements as described in the survey methodology The only geo-location accuracy information relevant for the transect module is thus the same as presented previously distance between the theoretical point and the GPS measured surveyed point Then the successive land covers and landscapes surveyed along the 250-m line are collected as a sequence without distance or geo-located information (5) Data descriptor of resulting database and (6) a Documentation table for users to quickly identify the differences of collected data between LUCAS campaigns micro-data and harmonised database The harmonised LUCAS product reduces the complexity and layered nature of the original LUCAS datasets The database’s novelty lies in the fact that for the first time users can query the whole LUCAS archive concurrently allowing for comparisons and combinations between all variables collected during the relevant reference years The homogeneity of the product facilitates the unearthing of temporal and spatial relations that were otherwise jeopardized by the physical separation between survey results by avoiding the burden of combing through the cumbersome documentation the user is now free to concentrate on the research thereby facilitating scientific discovery and analysis the product suffers from the shortcomings inherent in the source data surveyor or technology-related errors of precision while taking coordinates or measurements The harmonisation process itself also reveals some inconsistencies in the source data certain variables could not be harmonised between survey years These are mostly related to measurements of percentage or extent of coverage Where in the early stages of LUCAS surveyors were asked to fill in a multiple choice questionnaire in subsequent surveys the surveyor was asked to fill in the actual value in quantified units which makes it impossible for these variables to be translated into the user friendly version; therefore in these cases the variables of 2006 must remain in their original coding Additional information can be found in the comments section of the record descriptor All the processing is done with SQL with only column reordering and consistency checks being done in R The code is freely available under GPL (> = 3) license Using the Landsat archive to map crop cover history across the United States The European land use and cover area-frame statistical survey Agricultural survey methods 149–168 (2010) focussing on the European Area frame Survey LUCAS 12–27 (2002) Stratified sampling of satellite images with a systematic grid of points ISPRS Journal of Photogrammetry and Remote Sensing 59 Redesign sample for Land Use/Cover Area frame Survey (LUCAS) 2018 Eurostat. Technical reference document c-1: Instructions for surveyors. https://ec.europa.eu/eurostat/documents/205002/8072634/LUCAS2018-C1-Instructions.pdf (2018) Eurostat. LUCAS Quality Report 2015, https://ec.europa.eu/eurostat/documents/205002/769457/LUCAS+Quality+Report+2015 (2015) d’Andrimont, R. et al. Lucas copernicus 2018: Earth observation relevant in-situ data on land cover throughout the european union. Earth System Science Data Discussions 2020, 1–19 (2020). URL, https://essd.copernicus.org/preprints/essd-2020-178/ the largest expandable soil dataset for Europe: a review Using CORINE land cover and the point survey LUCAS for area estimation International Journal of Applied Earth Observation and Geoinformation 10 Land cover and land use diversity indicators in LUCAS 2009 data Land Quality and Land Use Information in the European Union 59–68 (2011) Crowdsourced street-level imagery as a potential source of in-situ data for crop monitoring Use of LUCAS LC point database for validating country-scale land cover maps A semi-automated approach for the generation of a new land use and land cover product for germany based on landsat time-series and lucas in-situ data The Potential of Open Geodata for Automated Large-Scale Land Use and Land Cover Classification Detecting flowering phenology in oil seed rape parcels with sentinel-1 and-2 time series Spatial and semantic effects of lucas samples on fully automated land use/land cover classification in high-resolution sentinel-2 data Eurostat. Lucas 2018 (land use/cover area frame survey). https://ec.europa.eu/eurostat/en/web/lucas/data/primary-data/2006 (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/BE_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/CZ_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/DE_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/ES_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/FR_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey. https://ec.europa.eu/eurostat/documents/205002/209869/IT_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/LU_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/HU_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/NL_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/PL_2006_0.xls (2006) Eurostat. Lucas 2006 (land use/cover area frame survey). https://ec.europa.eu/eurostat/documents/205002/209869/SK_2006_0.xls (2006) Eurostat. Lucas 2009 (land use/cover area frame survey). https://ec.europa.eu/eurostat/en/web/lucas/data/primary-data/2009 (2009) Eurostat. Lucas 2012 (land use/cover area frame survey). https://ec.europa.eu/eurostat/en/web/lucas/data/primary-data/2012 (2012) Eurostat. Lucas 2015 (land use/cover area frame survey). https://ec.europa.eu/eurostat/en/web/lucas/data/primary-data/2015 (2015) Eurostat. Lucas 2018 (land use/cover area frame survey). https://ec.europa.eu/eurostat/en/web/lucas/data/primary-data/2018 (2018) Eurostat. Contents of the 2006 lucas primary data). https://ec.europa.eu/eurostat/documents/205002/209869/Contents_LUCAS_2006_primary_data.xls (2006) Eurostat. Contents of the 2012 lucas primary data). https://ec.europa.eu/eurostat/documents/205002/208012/Contents-LUCAS-primary-data-12-20140618-.xls (2012) Eurostat. Technical reference document c-1: Instructions for surveyors. https://ec.europa.eu/eurostat/documents/205002/209869/LUCAS2006_C1-Instructions_20110204.pdf (2006) Eurostat. Technical reference document c-1: Instructions for surveyors. https://ec.europa.eu/eurostat/documents/205002/208938/LUCAS+2009+Instructions (2009) Eurostat. Technical reference document c-3: Classification. https://ec.europa.eu/eurostat/documents/205002/209869/LUCAS2006_C3-Classification_20131004.pdf (2006) Eurostat. Technical reference document c-3: Classification. https://ec.europa.eu/eurostat/documents/205002/208938/LUCAS2009_C3-Classification_20121004.pdf (2009) Eurostat. Technical reference document c-3: Classification. https://ec.europa.eu/eurostat/documents/205002/8072634/LUCAS2018-C3-Classification.pdf (2018) Eurostat. Lucas survey 2015 web csv record descriptor. https://ec.europa.eu/eurostat/documents/205002/6786255/WebCsv_RecordDescriptor20161006.pdf (2016) Eurostat. Lucas survey 2018 web csv record descriptor. https://ec.europa.eu/eurostat/documents/205002/8072634/LUCAS2018-RecordDescriptor-190611.pdf (2019) d’Andrimont, R. et al. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union. European Commission, Joint Research Centre (JRC) https://doi.org/10.2905/f85907ae-d123-471f-a44a-8cca993485a2 (2020) d’Andrimont, R. et al. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union. figshare https://doi.org/10.6084/m9.figshare.9962765.v2 (2020) Yordanov, M., Martinez, L. & d’Andrimont, R. LUCAS R PAckage. CRAN repository, https://cran.r-project.org/web/packages/lucas/index.html (2020) ESTAT. Land cover and land use, landscape (LUCAS) (lan). https://ec.europa.eu/eurostat/cache/metadata/en/lan_esms.htm (2019) Eurostat. Technical reference document C-4: Quality Control Procedure, https://ec.europa.eu/eurostat/documents/205002/208938/LUCAS2009_C4-QCProcedures_20090303.pdf (2009) Eurostat. Technical reference document C-4: Quality Control Procedure, https://ec.europa.eu/eurostat/documents/205002/208012/LUCAS2012_C4-QCProcedures_20120113.pdf (2012) Eurostat. Technical reference document C-4: Quality Control Procedure. https://ec.europa.eu/eurostat/documents/205002/6786255/LUCAS2015-C4-QCProcedures-20150227.pdf (2015) R: A language and environment for statistical computing (2013) Download references The authors would like to acknowledge the many surveyors and support personal who have been carrying out the LUCAS survey we would like to thank members of the Eurostat LUCAS team (M Martino,…) who have been instrumental over the years in implementing LUCAS at various stages Elvekjaer for her precious comments on the manuscript The authors would also like to thank the Google Earth Engine team for their support in the harmonisation process The authors would also like to thank the JRC Big Data Platform (JEODPP) for the support provided European Commission Joint Research Centre (JRC) are responsible of the LUCAS data collection provides a storage solution to distribute the photos reviewed the DB and made the documentation table provided comments and suggestions on the manuscript The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article Download citation DOI: https://doi.org/10.1038/s41597-020-00675-z Sorry, a shareable link is not currently available for this article. Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly. By continuing to use this website, you agree to the use of cookies in order to offer you content and services that are tailored to your interests. Hordes of children will come together and howl as they dance alone on the ashes like wild bears It only takes one shout to wake them all from their slumber Animatricks Animation FestivalFinland Stuttgart Trickfilm International Animated Film Festival Germany Cesar Awards - French film industry awardsFrance View more(24)FermerAnd then the BearSelections(24)Anim'EstRomania TAAFJapan Young French CinemaUnited States Anim'EstRomania Stuttgart Trickfilm International Animated Film Festival Germany Aspen ShortsfestUnited States Clermont-Ferrand International Short Film FestivalFrance Prague International Short Film Festival Czech Republic Aix-en-Provence Tous Courts Short Film FestivalFrance CinanimaPortugal Leeds International Film FestivalGreat Britain International Animation Film Festival in Erevan (ReAnimania)Armenia BIAFSouth Korea Ghent Film FestivalBelgium AnimatouSwitzerland TIFF (Toronto International Film Festival)Canada Festival Internacional de Curtas de São PauloBrazil Melbourne International Film FestivalAustralia Festival du Film court en Plein air de GrenobleFrance Cinema Jove - Valencia International Film FestivalSpain Cannes International Film FestivalFrance Prix du meilleur scénario : Agnès Patron View more(7)FermerAnd then the BearAwards(7)Cesar Awards - French film industry awardsFrance Oliver Townend claimed a stunning title double in the Festival of British Eventing at Gatcombe that underlined his outstanding form this season. The 32-year-old Yorkshireman won the Dodson and Horrell British Novice Championship with Sonic De Sermentol - he was also second with Ridire Dorcha - and he took the Smith and Williamson Intermediate crown on Note Worthy, a class he won by 12 penalties. And he lies second and third in the British Open aboard Mr Hiho and Armada, respectively, behind leader Andrew Hoy and Rutherglen heading into Sunday's showjumping and cross-country phases. 'I will be giving it my best shot, that's for sure,' said Townend, assessing the possibility of a Gatcombe hat-trick. 'I will never stop trying.' Reflecting on his success in the novice section, Townend added: 'This is Sonic De Sermentol's first season eventing. 'His owner, Stephanie d'Andrimont, bought him as a three-year-old from an auction in France, but he grew too big for her. I told Stephanie that I thought he would win this class a few months ago. 'And Note Worthy is a class horse that really gallops and jumps. I was drawn early on and I knew I had good ones around me, so I couldn't afford to hang around in the cross-country.  Townend claims brilliant double in the Festival of British EventingCommenting on this article has endedNewest{{#isModerationStatus}}{{moderationStatus}}