{"id":693,"date":"2023-01-26T06:53:43","date_gmt":"2023-01-26T06:53:43","guid":{"rendered":"https:\/\/garslab.com\/?p=693"},"modified":"2023-12-27T03:43:38","modified_gmt":"2023-12-27T03:43:38","slug":"%e5%85%a8%e7%90%83%e6%b2%bf%e6%b5%b7%e6%a0%b8%e7%94%b5%e7%ab%99%e6%b8%a9%e6%8e%92%e6%b0%b4%e7%83%ad%e7%be%bd%ef%bc%88gcnt-plume%ef%bc%89%e6%95%b0%e6%8d%ae%e9%9b%86","status":"publish","type":"post","link":"https:\/\/garslab.com\/?p=693&lang=zh-hans","title":{"rendered":"\u5168\u7403\u6cbf\u6d77\u6838\u7535\u7ad9\u6e29\u6392\u6c34\u70ed\u7fbd\u6570\u636e\u96c6"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">\u6570\u636e\u63cf\u8ff0<\/h2>\n\n\n\n<p>GCNT-Plume\u6570\u636e\u96c6\u63d0\u4f9b\u4e86\u5168\u7403\u6cbf\u6d77\u548c\u4e94\u5927\u6e56\u5730\u533a66\u4e2a\u6838\u7535\u7ad9\u5171\u8ba174\u4e2a\u6392\u6c34\u53e3\u5728\u8fd0\u884c\u671f\u95f4\u91ca\u653e\u7684\u6e29\u6392\u6c34\u70ed\u7fbd\u7684\u957f\u671f\u53d1\u751f\u9891\u7387\u53ca\u6e29\u5347\u5f3a\u5ea6\u7684\u7a7a\u95f4\u5206\u5e03\u3002\u8be5\u6570\u636e\u96c6\u5229\u7528Landsat\u536b\u661f\u70ed\u7ea2\u5916\u5f71\u50cf\u89c2\u6d4b\u7684\u70ed\u5f02\u5e38\uff0c\u901a\u8fc7\u91c7\u7528U-Net\u6a21\u578b\u548c\u6574\u5408\u6392\u6c34\u53e3\u4f4d\u7f6e\u7b49\u5148\u9a8c\u77e5\u8bc6\u8fdb\u884c\u5206\u5272\u751f\u6210\u3002\u8fd9\u4e00\u6570\u636e\u96c6\u4e3a\u5b9e\u5730\u91c7\u6837\u548c\u52a8\u529b\u8fc7\u7a0b\u5efa\u6a21\u63d0\u4f9b\u4e86\u57fa\u51c6\u8303\u56f4\uff0c\u6709\u52a9\u4e8e\u6df1\u5165\u7814\u7a76\u4eba\u4e3a\u6e29\u6392\u6c34\u5bf9\u6c34\u751f\u6001\u73af\u5883\u5f71\u54cd\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"667\" height=\"870\" src=\"http:\/\/garslab.com\/wp-content\/uploads\/2023\/12\/10.jpg\" alt=\"\" class=\"wp-image-823\" style=\"width:837px;height:auto\" srcset=\"https:\/\/garslab.com\/wp-content\/uploads\/2023\/12\/10.jpg 667w, https:\/\/garslab.com\/wp-content\/uploads\/2023\/12\/10-230x300.jpg 230w, https:\/\/garslab.com\/wp-content\/uploads\/2023\/12\/10-115x150.jpg 115w\" sizes=\"auto, (max-width: 667px) 100vw, 667px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u6570\u636e\u4e0b\u8f7d<\/h2>\n\n\n\n<p>GCNT-Plume\u6570\u636e\u96c6\u53ef\u901a\u8fc7zenodo\u5e73\u53f0\u83b7\u5f97\uff1a<\/p>\n\n\n\n<p><a href=\"https:\/\/zenodo.org\/records\/8144425\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/zenodo.org\/records\/8144425<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u76f8\u5173\u6587\u732e<\/h2>\n\n\n\n<p>Wei, J., Feng, L.*, Tong, Y.*, Xu, Y., &amp; Shi, K. (2023). Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning. Remote Sensing of Environment, 295, 113707.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0034425723002584\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0034425723002584<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6570\u636e\u63cf\u8ff0 GCNT-Plume\u6570\u636e\u96c6\u63d0\u4f9b\u4e86\u5168\u7403\u6cbf\u6d77\u548c\u4e94\u5927\u6e56\u5730\u533a66\u4e2a\u6838\u7535\u7ad9\u5171\u8ba174\u4e2a\u6392\u6c34\u53e3\u5728\u8fd0\u884c\u671f\u95f4\u91ca\u653e\u7684\u6e29\u6392\u6c34\u70ed\u7fbd\u7684\u957f\u671f\u53d1\u751f\u9891\u7387\u53ca\u6e29\u5347\u5f3a\u5ea6\u7684\u7a7a\u95f4\u5206\u5e03\u3002\u8be5\u6570\u636e\u96c6\u5229\u7528Landsat\u536b\u661f\u70ed\u7ea2\u5916\u5f71\u50cf\u89c2\u6d4b\u7684\u70ed\u5f02\u5e38\uff0c\u901a\u8fc7\u91c7\u7528U-Net\u6a21\u578b\u548c\u6574\u5408\u6392\u6c34\u53e3\u4f4d\u7f6e\u7b49\u5148\u9a8c\u77e5\u8bc6\u8fdb\u884c\u5206\u5272\u751f\u6210\u3002\u8fd9\u4e00\u6570\u636e\u96c6\u4e3a\u5b9e\u5730\u91c7\u6837\u548c\u52a8\u529b\u8fc7\u7a0b\u5efa\u6a21\u63d0\u4f9b\u4e86\u57fa\u51c6\u8303\u56f4\uff0c\u6709\u52a9\u4e8e\u6df1\u5165\u7814\u7a76\u4eba\u4e3a\u6e29\u6392\u6c34\u5bf9\u6c34\u751f\u6001\u73af\u5883\u5f71\u54cd\u3002 \u6570\u636e\u4e0b\u8f7d GCNT-Plume\u6570\u636e\u96c6\u53ef\u901a\u8fc7zenodo\u5e73\u53f0\u83b7\u5f97\uff1a https:\/\/zenodo.org\/records\/8144425 \u76f8\u5173\u6587\u732e Wei, J., Feng, L.*, Tong, Y.*, Xu, Y., &amp; Shi, K. (2023). Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning. Remote Sensing of Environment, 295, 113707. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0034425723002584<\/p>\n","protected":false},"author":2,"featured_media":694,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13,16],"tags":[],"class_list":["post-693","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-13","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5168\u7403\u6cbf\u6d77\u6838\u7535\u7ad9\u6e29\u6392\u6c34\u70ed\u7fbd\u6570\u636e\u96c6 - Global Aqua Remote Sensing (GARS) laboratory<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/garslab.com\/?p=693\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5168\u7403\u6cbf\u6d77\u6838\u7535\u7ad9\u6e29\u6392\u6c34\u70ed\u7fbd\u6570\u636e\u96c6 - Global Aqua Remote Sensing (GARS) laboratory\" \/>\n<meta property=\"og:description\" content=\"\u6570\u636e\u63cf\u8ff0 GCNT-Plume\u6570\u636e\u96c6\u63d0\u4f9b\u4e86\u5168\u7403\u6cbf\u6d77\u548c\u4e94\u5927\u6e56\u5730\u533a66\u4e2a\u6838\u7535\u7ad9\u5171\u8ba174\u4e2a\u6392\u6c34\u53e3\u5728\u8fd0\u884c\u671f\u95f4\u91ca\u653e\u7684\u6e29\u6392\u6c34\u70ed\u7fbd\u7684\u957f\u671f\u53d1\u751f\u9891\u7387\u53ca\u6e29\u5347\u5f3a\u5ea6\u7684\u7a7a\u95f4\u5206\u5e03\u3002\u8be5\u6570\u636e\u96c6\u5229\u7528Landsat\u536b\u661f\u70ed\u7ea2\u5916\u5f71\u50cf\u89c2\u6d4b\u7684\u70ed\u5f02\u5e38\uff0c\u901a\u8fc7\u91c7\u7528U-Net\u6a21\u578b\u548c\u6574\u5408\u6392\u6c34\u53e3\u4f4d\u7f6e\u7b49\u5148\u9a8c\u77e5\u8bc6\u8fdb\u884c\u5206\u5272\u751f\u6210\u3002\u8fd9\u4e00\u6570\u636e\u96c6\u4e3a\u5b9e\u5730\u91c7\u6837\u548c\u52a8\u529b\u8fc7\u7a0b\u5efa\u6a21\u63d0\u4f9b\u4e86\u57fa\u51c6\u8303\u56f4\uff0c\u6709\u52a9\u4e8e\u6df1\u5165\u7814\u7a76\u4eba\u4e3a\u6e29\u6392\u6c34\u5bf9\u6c34\u751f\u6001\u73af\u5883\u5f71\u54cd\u3002 \u6570\u636e\u4e0b\u8f7d GCNT-Plume\u6570\u636e\u96c6\u53ef\u901a\u8fc7zenodo\u5e73\u53f0\u83b7\u5f97\uff1a https:\/\/zenodo.org\/records\/8144425 \u76f8\u5173\u6587\u732e Wei, J., Feng, L.*, Tong, Y.*, Xu, Y., &amp; Shi, K. 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