https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Zhang%2A_et_al_2024a 2026-05-08T19:26:02Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Zhang*_et_al_2024a&diff=302734&oldid=prev 2024-06-10T10:24:40Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_392737377" class="mw-redirect" title="Draft Sanchez Pinedo 392737377">Draft Sanchez Pinedo 392737377</a> to <a href="/public/Zhang*_et_al_2024a" title="Zhang* et al 2024a">Zhang* et al 2024a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 10:24, 10 June 2024</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

JSanchez https://www.scipedia.com/wd/index.php?title=Zhang*_et_al_2024a&diff=302733&oldid=prev 2024-06-10T10:24:33Z

<p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 10:24, 10 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Precise identification of levee relief well locations from satellite imagery can be challenging and time-consuming. In this research, we propose a novel approach utilizing a U-Net architecture to identify relief wells along flood control levees, as a proof of concept of combining freely available imagery with machine learning techniques. The goal of this research is to develop techniques to make infrastructure asset management more efficient by automating the process. Our study highlights the crucial role of the convolution kernel size in the U-Net architecture, which significantly influences the accuracy of the results. Larger convolution kernel sizes excel in capturing extensive contextual information from the input image, potentially leading to superior outcomes. However, training with larger kernels is computationally intensive. Conversely, smaller convolution kernel sizes excel in capturing local features.&#160; To strike a balance these considerations, we introduce a Dual Kernel Filter U-Net, which combines two U-Nets with distinct convolution kernel sizes, 3x3 and 11x11. This innovative approach aims to harness the strengths of both convolution kernel sizes to improve accuracy and overall performance. The proposed Dual kernel filter U-Net was trained and evaluated on a real dataset of relief wells from Google Earth imagery. Our evaluation results demonstrate that this model achieves an accuracy rate of 99.76% with training dataset 2626 satellite images. Notably, this significant accuracy enhancement is achieved without substantially increasing computational time, making it a promising advancement in satellite image analysis for object location identification and asset, and disaster management</div></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Precise identification of levee relief well locations from satellite imagery can be challenging and time-consuming. In this research, we propose a novel approach utilizing a U-Net architecture to identify relief wells along flood control levees, as a proof of concept of combining freely available imagery with machine learning techniques. The goal of this research is to develop techniques to make infrastructure asset management more efficient by automating the process. Our study highlights the crucial role of the convolution kernel size in the U-Net architecture, which significantly influences the accuracy of the results. Larger convolution kernel sizes excel in capturing extensive contextual information from the input image, potentially leading to superior outcomes. However, training with larger kernels is computationally intensive. Conversely, smaller convolution kernel sizes excel in capturing local features.&#160; To strike a balance these considerations, we introduce a Dual Kernel Filter U-Net, which combines two U-Nets with distinct convolution kernel sizes, 3x3 and 11x11. This innovative approach aims to harness the strengths of both convolution kernel sizes to improve accuracy and overall performance. The proposed Dual kernel filter U-Net was trained and evaluated on a real dataset of relief wells from Google Earth imagery. Our evaluation results demonstrate that this model achieves an accuracy rate of 99.76% with training dataset 2626 satellite images. Notably, this significant accuracy enhancement is achieved without substantially increasing computational time, making it a promising advancement in satellite image analysis for object location identification and asset, and disaster management</div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_392737377104.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:302731:newid:302733 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Zhang*_et_al_2024a&diff=302731&oldid=prev 2024-06-10T10:24:32Z

<p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 10:24, 10 June 2024</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'>&#160;</td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2">&#160;</td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Precise identification of levee relief well locations from satellite imagery can be challenging and time-consuming. In this research, we propose a novel approach utilizing a U-Net architecture to identify relief wells along flood control levees, as a proof of concept of combining freely available imagery with machine learning techniques. The goal of this research is to develop techniques to make infrastructure asset management more efficient by automating the process. Our study highlights the crucial role of the convolution kernel size in the U-Net architecture, which significantly influences the accuracy of the results. Larger convolution kernel sizes excel in capturing extensive contextual information from the input image, potentially leading to superior outcomes. However, training with larger kernels is computationally intensive. Conversely, smaller convolution kernel sizes excel in capturing local features.&#160; To strike a balance these considerations, we introduce a Dual Kernel Filter U-Net, which combines two U-Nets with distinct convolution kernel sizes, 3x3 and 11x11. This innovative approach aims to harness the strengths of both convolution kernel sizes to improve accuracy and overall performance. The proposed Dual kernel filter U-Net was trained and evaluated on a real dataset of relief wells from Google Earth imagery. Our evaluation results demonstrate that this model achieves an accuracy rate of 99.76% with training dataset 2626 satellite images. Notably, this significant accuracy enhancement is achieved without substantially increasing computational time, making it a promising advancement in satellite image analysis for object location identification and asset, and disaster management</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:302730:newid:302731 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Zhang*_et_al_2024a&diff=302730&oldid=prev 2024-06-10T10:24:30Z

<p>Created blank page</p> <p><b>New page</b></p><div></div>

JSanchez