{"_buckets": {"deposit": "337648a5-ef06-48ad-bd44-bbbb83dcc901"}, "_deposit": {"created_by": 45, "id": "6633", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "6633"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00006633", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Land Use Classification using Deep Convolutional Neural Network", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "One of the challenging issues in high-resolution remote\nsensing images is classifying land-use scenes with high\nquality and accuracy Land use classification is required\nto measure land and its impact on ecosystem. Deep\nlearning is a powerful state-of-the-art technique for\nimage processing including remote sensing images.\nLand use is classified for environmental monitoring,\nurban planning and resource management. This\nproposed system will use in the UC Merced land-use\ndata set. The preprocessing the image can make the\nimproving of image positional accuracy, reducing the\nstorage space, the improving the spectral qualities of\nimage. The pretrained CNN is initially used to learn\ndeep and robust features. Then, the feature extractor of\nCNN mapps the features and the fully connected layers\nof CNN are used to obtain excellent results."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Classification"}, {"interim": "Deep Learning"}, {"interim": "Land Use"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-11-26"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Land Use Classification using Deep Convolutional Neural Network.pdf", "filesize": [{"value": "349 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 349000.0, "url": {"url": "https://meral.edu.mm/api/files/337648a5-ef06-48ad-bd44-bbbb83dcc901/Land%20Use%20Classification%20using%20Deep%20Convolutional%20Neural%20Network.pdf"}, "version_id": "03d228ea-13e8-4df2-b8d0-e5826315cc01"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICAIT-2017", "subitem_c_date": "1-2 November, 2017", "subitem_conference_title": "1st International Conference on Advanced Information Technologies", "subitem_place": "Yangon, Myanmar", "subitem_session": "Workshop Session", "subitem_website": "https://www.uit.edu.mm/icait-2017/"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Su Wai Tun"}, {"subitem_authors_fullname": "Khin Mo Mo Tun"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2017-11-02"}, "item_title": "Land Use Classification using Deep Convolutional Neural Network", "item_type_id": "21", "owner": "45", "path": ["1605779935331"], "permalink_uri": "https://meral.edu.mm/records/6633", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-11-26"}, "publish_date": "2020-11-26", "publish_status": "0", "recid": "6633", "relation": {}, "relation_version_is_last": true, "title": ["Land Use Classification using Deep Convolutional Neural Network"], "weko_shared_id": -1}