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"fi": "Lämpösummamalli kuvaa Suomen rannikkoalueiden välisiä eroja keväisessä lämpenemisnopeudessa ajanjaksolla jäidenlähdöstä heinäkuun 15. päivään asti vuonna 2014. Lämpösumma on mallinnettu käyttämällä lämpötilaloggeridataa sekä Ilmatieteenlaitoksen mareografi- ja aaltopoijutietoja. Tiedosto on rasterimuotoinen, sen spatiaalinen resoluution 50 m ja se kattaa koko Suomen rannikkoalueen. Aineistoa käytetään mm. kalojen habitaattimallinnuksen taustamuuttujana.",
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"en": "The data consists of data from the Berry Machine project. The project has aimed to develop an AI-based system to improve berry observations and forecasts in observation forests.\n\nThe Natural Resources Institute Finland has been making observations and forecasts of berry harvests since the 1990s. The observations have been based on plots in the forests. Each forest has five observation plots of one square metre. Flowers, raw berries and ripe berries are counted during the growing season and forecasts are based on these calculations. \n\nThe aim of the Berry Machine project was to develop a machine-learning system that can be used to estimate the quantity and quality of berry harvests using a photo taken with a smartphone. The target was blueberry (Vaccinium Myrtillus) and lingonberry (Vaccinium Vitis-idea)\n\nA prototype application was implemented on a smart phone to determine the number of bilberry and lingonberry flowers, raw berries and ripe berries using machine vision. To determine the area of the observation area needed for berry density, AR-based measurement was tested in the prototype.\n\nThe ratio of the number of berries interpreted from the image to the actual value was compared with the help of the field measurements. The number of berries detected by machine learning and the result of the field measurement were attached to the same image.\n\nFor the study of berry density estimation using an image, the area of berries detected by machine learning was determined by cropping the detected berries by the boundaries of the (annotated) count frame drawn on the same image. The bounding with polygon verifies that berries have been detected in the image are exactly in the area corresponding to the area of the frame.\n\nThe dataset is in three parts:\n\n 1. 640x640 Images with VOC annotations. 640x640 annotated images ready for machine learning.\n 2. datatables: datasheets containing metadata about the images, inference results of the trained machine learning model on the images, and information about the count frames and images with field measurement berry density\n 3. Original. Original image files and annotations (in COCO format). 80GB of original material is split into eight zip files for easy downloading. Also frame annotation polygons (COCO-format)\n\nThe data in the tables can be linked to each other via the image file path. For a more detailed description of the data, see README.md. \n\nThe project's AI system is prototyped and implemented by the FrostBit Software Laboratory at Lapland UAS.",
"fi": "Data koostuu Marjamasiina-hankkeen aineistosta. Hankkeessa on pyritty kehittämään tekoälyä hyödyntävä järjestelmä, jolla helpotetaan marjahavaintojen -ja ennusteiden tekemistä havaintometsissä.\n\nLuonnonvarakeskus on tehnyt marjasadon havaintoja ja ennusteita 1990-luvulta lähtien. Havainnot ovat perustuneet metsissä oleviin koealoihin. Jokaisessa metsässä on viisi neliömetrin kokoista havaintoruutua. Kukat, raakileet ja kypsät marjat lasketaan kasvukauden aikana ja ennusteet perustuvat näihin laskelmiin. \n\nMarjamasiina-hankkeen tarkoituksena oli kehittää koneoppiminen, jonka avulla voidaan marjasadon määrän sekä laadun arvioimisessa käyttää älypuhelimella otettua valokuvaa. Kohteena oli mustikka (Vaccinium Myrtillus) sekä puolukka (Vaccinium Vitis-idea)\n\nÄlypuhelimelle toteutettiin ainestolla prototyyppisovellus joka määrittää mustikan sekä puolukan kukkien, raakileiden ja kypsien marjojen määrän konenäön avulla. Marjatiheydessä tarvittavan havaintoalueen pinta-alan määrittämisessä testattiin protoyypissä AR-pohjaista mittausta.\n\nKuvasta tulkittavan marjamäärän suhdetta maastossa laskettuun määrään verrattiin maastolaskennan avulla. Kuvaan laskentakehikosta liitettiin tieto maastolaskennen tuloksesta ja koneoppimisen havaitsemien marjojen määrästä samassa kuvassa.\n\nKuvan avulla marjatiheyden määrittämisen tutkimisessa koneoppimisen havaitsemien marjojen pinta-ala määritettiin rajaamalla havaitut marjat samaan kuvaan piirretyn (annotoidun) laskentakehikon rajoilla. Laskentakehällä rajaamalla varmennetaan, että kuvassa on havaittu marjoja täsmälleen kehikon pinta-alan vastaavalta alueelta.\n\nData sisältää kolme aineistoa:\n\n 1. 640x640 Images with VOC annotations. Koneoppimiselle koulutusvalmiit 640x640 annotoidut kuvat\n 2. Datatables. Datataulut, joissa metatietoa kuvista, koulutetun koneoppimismallin havainnot kuvista ja tietoa laskentakehikoista sekä kuvista joiden marjatiheys voidaan arvioida\n 3. Original. Aluperäiset kuvatiedostot ja annotoinnit (COCO-formaatissa). 80GB alkuperäinen ainesto on jaettu kahdeksaan zip-tiedostoon tiedostojen latauksen helpottamiseksi. Myös laskentakehikoiden polygonit annotoituna (COCO-formaatti)\n\nTaulukoiden tieto on toisiinsa liitettävissä kuvan tiedostopolulla. Tarkempi aineistokuvaus tiedostossa README.md. \n\nHankkeen tekoälyjärjestelmän prototypoinnista ja toteutuksesta vastaa Lapin AMK:n FrostBit ohjelmistolaboratorio."
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