地圖人工智慧 (Map AI)發展
地圖人工智慧 (Map AI)發展
2023/09/19
2023/10/29
<pre>2023數位人文研究計畫成果展</pre><br>
展覽頁面呈現形式
橫式
策展者
地理資訊數位加值與地圖人工智慧發展計畫
展覽封面
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展覽資料
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"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 & 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>",
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"desc": "基於地方志影像集及人工分類標註資料,嘗試八個最新的深度卷積神經網路,挑選出DaViT分類準確率最高為 93.9%,F1 分數為 90.6%。研究結果能針對大量地方影像進行快速分類組織,有助於研究人員進行後續影像關連性及風格研究,相同的技術也可以應用於其他數位人文領域的藝術和歷史視覺材料上。同時,建立地方志影像分類線上工具,使用者僅需要上傳低解析度影像就可以達到高正確率的分類預測,也可以兼顧版權保護的考量。<br><div><br></div><div>研究論文:Jhe-An Chen, Jen-Chien Hou, Richard Tzong-Han Tsai, Hsiung-Ming Liao, Shih-Pei Chen, and Ming-Ching Chang Image Classification for Historical Documents: A Study on Chinese Local Gazetteers,Digital Scholarship in the Humanities (In print)</div>",
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