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[{"key": "dc.contributor.advisor", "value": "P\u00f6l\u00f6nen, Ilkka", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Uusitalo, Laura", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "M\u00e4yr\u00e4, Janne", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2018-12-19T13:22:59Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2018-12-19T13:22:59Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2018", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/60705", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Syv\u00e4oppiminen saanut paljon huomiota 2000-luvun puoliv\u00e4list\u00e4 alkaen, ja t\u00e4n\u00e4 p\u00e4iv\u00e4n\u00e4 sen sovelluksia on l\u00e4hes kaikkialla. Samalla aikav\u00e4lill\u00e4 avoimen satelliittikuvadatan m\u00e4\u00e4r\u00e4 on kasvanut, erityisesti Sentinel-2 satelliittien laukaisujen j\u00e4lkeen. T\u00e4t\u00e4 dataa voidaan hy\u00f6dynt\u00e4\u00e4 useissa kaukokartoitussovellutuksissa, mutta t\u00e4m\u00e4n datam\u00e4\u00e4r\u00e4n analysointi ja k\u00e4sittely on ihmisille k\u00e4yt\u00e4nn\u00f6ss\u00e4 mahdotonta. T\u00e4ss\u00e4 tutkielmassa testattiin er\u00e4\u00e4n tunnetun neuroverkkoarkkitehtuurin, U-Netin, suorituskyky\u00e4 Rakkolanjoen valuma-alueen maanpeiteluokittelussa monispektrisatelliittikuvista eri luokittelutarkkuuksille. Eri l\u00e4htodatoilla saatuja luokittelutarkkuuksia vertailtiin kesken\u00e4\u00e4n, ja parhaat luokittelutulokset saatiin hy\u00f6dynt\u00e4m\u00e4ll\u00e4 sek\u00e4 kaikkea Sentinel-2 dataa ett\u00e4 erikseen laskettuja spektri-indeksej\u00e4. Huolimatta l\u00e4hes olemattomasta verkkojen hienos\u00e4\u00e4d\u00f6st\u00e4 ja lyhyest\u00e4 koulutusajasta saadut luokittelutulokset ovat varsin lupaavia helpoimman luokittelutason (CORINE land cover taso 1) tarkkuuden ollessa yli 90% ja haastavimmallakin yli 75%. Tuotettuja maanpeitekarttoja vertailtiin my\u00f6s visuaalisesti sek\u00e4 l\u00e4ht\u00f6tietoihin ett\u00e4 satelliittikuviin. Johtop\u00e4\u00e4t\u00f6ksen\u00e4 voidaan todeta, ett\u00e4 U-Net on k\u00e4ytt\u00f6kelpoinen malli Suomen Ymp\u00e4rist\u00f6keskuksen tarpeisiin, ja kehitetty\u00e4 mallia tullaan jatkokehitt\u00e4m\u00e4\u00e4n edelleen.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Since the mid 2000's, deep learning has received much attention and today its applications are almost everywhere. Around the same timespan the amount of freely available satellite data has grown, especially after Sentinel-2 missions started. This data has a lot of remote sensing applications, but the amount of produced data is practically impossible for humans to analyze or process. This thesis tested the viability of U-Net, a well-known neural network architecture, in land cover classification from multispectral satellite images to different classification levels in the Rakkolanjoki river drainage basin area. Classification results from only visible light bandwidths, all Sentinel-2 bands, precomputed spectral indices and all available features were compared, and best results were achieved with all available features. Even with next to none fine-tuning and short training time, implemented version of U-Net managed to accurately classify over 90% of the pixels for the easiest classification level (CORINE land cover level 1), and around 75% for the hardest level. Produced segmentation maps were also visually observed and compared to both ground truth labels and RGB-composites of the satellite image. As as conclusion, U-Net is a viable baseline for the needs of Finnish Environment Institute, and will later be developed further.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2018-12-19T13:22:59Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2018-12-19T13:22:59Z (GMT). No. of bitstreams: 0\n Previous issue date: 2018", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "84", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "land cover classification", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Land cover classification from multispectral data using convolutional autoencoder networks", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-201812195243", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.collaborator", "value": "public", "language": "", "element": "contractresearch", "qualifier": "collaborator", "schema": "yvv"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "yvv.contractresearch.initiative", "value": "university", "language": "", "element": "contractresearch", "qualifier": "initiative", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "602", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "neuroverkot", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "kaukokartoitus", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "satelliittikuvat", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "satelliittikuvaus", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "neural networks", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "remote sensing", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "satellite images", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "machine learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "satellite photography", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.type.okm", "value": "G2", "language": null, "element": "type", "qualifier": "okm", "schema": "dc"}]
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