地圖人工智慧 (Map AI)發展

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}, "title": "嘗試3種風格轉換模型產製合成歷史地圖", "desc": "本研究運用Pix2Pix、CycleGAN和CUT模型進行地圖風格轉換， 產製出合成歷史地圖(Synthesized historical maps)。 實驗結果，CycleGAN和CUT成功地將當代地圖圖磚轉移成歷史風格，可以發現CUT產製的圖磚中的橋樑符號比 CycleGAN 製作的結果更近似於歷史地圖。<br>", "musicName": "無", "weight": 7, "musicText": "", "musicURL": "" }, "text-s2yydxem80": { "id": "text-s2yydxem80", "type": "text", "sortable": true, "musicName": "無", "weight": 8, "desc": "<span><div><div>本研究最終基於U-Net網路設計了一套半監督式學習的演算法，從實驗結果證明可由非向量化歷史地圖影像中自動識別出橋梁符號，並能達到超過80%的準確率，該研究所發展出來的方法及流程，對於大量歷史地理圖資向量化或特徵抽取帶來極大的應用潛力。</div><div><br></div></div><div><div><a target=\"_blank\" rel=\"nofollow\"></a>研究論文：Cheng-Shih Wong, Hsiung-Ming Liao, Richard Tzong-Han Tsai &amp; Ming-Ching Chang, 2022, Semi-supervised learning for topographic map analysis over time: a study of bridge segmentation. Scientific Reports 12, 18997 (2022). </div></div><div><div><br></div><div>全文下載：<a target=\"_blank\" rel=\"nofollow\" href=\"https://doi.org/10.1038/s41598-022-23364-w?fbclid=IwAR0O7e79H44GVeQrtMpl8bsclHx3oDDOueWy5r7ESgN32cTaAERu5F7oBhU\">https://doi.org/10.1038/s41598-022-23364-w</a></div></div></span><br>", "title": "小結", "musicText": "", "musicURL": "" }, "theme-9t3rq3qblv": { "id": "theme-9t3rq3qblv", "type": "theme", "sortable": true, "primaryObj": { "oid": "f42bbe8c5713a23cbc6ca33eb2dfbb07", "fileType": "image", "fileURI": "https://openmuseum.tw/files/muse_portal/muse_styles/w1024/mcode/13340e98266803a9d2252c2ccebd75ab.jpg?itok=SWfLeXlJ", "level": "primary", "title": "2023成果展底圖/Map_AI底圖2", "desc": "", "thumbURI": "https://openmuseum.tw/files/muse_portal/muse_styles/square_thumbnail_100/mcode/13340e98266803a9d2252c2ccebd75ab.jpg?itok=PWNaYn7c", "thumbMediumURI": 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