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Application Of Image Processing To Detect Infrastructure Damage Caused By Earthquakes

Application Of Image Processing To Detect Infrastructure Damage Caused By Earthquakes. Presented By K.ALHAF MALIK PreFinal Year Syed Ammal Engg College. Introduction. The Ministry of Land, Infrastructure and Transport services play important role when disaster occur.

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Application Of Image Processing To Detect Infrastructure Damage Caused By Earthquakes

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  1. Application Of Image Processing To Detect Infrastructure Damage Caused By Earthquakes Presented By K.ALHAF MALIK PreFinal Year Syed Ammal Engg College

  2. Introduction • The Ministry of Land, Infrastructure and Transport services play important role when disaster occur. • So at that time appropriate crisis management is required. • In this paper presenting about matters of using remote sensing technologies. • Then the applicability of image processing in order to detect damage from images is presented.

  3. Remote Sensing Technologies • There are restrictions considered in using remote sensing technologies they are • Lead Time & Weather time • High Resolution Artificial Satellites • Aircraft & Helicopter • Resolution • 0.2 to 0.5m – all types of damage are detected • 1 to 2m- small damage like horizontal and vertical difference • 10m- most of damage cannot be detected

  4. Applicability of Image Processing In detecting damaged facilities based on the remote sensing images, one of factors how accurately and rapidly damaged facilities are detected is the applicability if image processing The two main classifications of damage are Liner-type damage Collapsed Bridges and Slope failure on embankment Areal-type damage Liquefaction and slope failure on mountains and all wide spread damages

  5. Trail To Detect Damage By Image Processing Easiness and rapidness are important factors when you select image processing to detect damage because it is necessary to grasp disaster situation as soon as possible. Horizontal difference at a viaduct In the case of liner facilities, damage appears as blurs. To grasp this blurs 1) edge extraction, 2) texture analysis, and 3) edge enhancement are applied in this trial.

  6. Slope failure (Areal-type damage) The change of color at the surface gives us the information to detect and specify the spread of slope failure. To extract damage, in this trial 1) edge extraction, 2) unsupervised classification, texture analysis, 3) edge enhancement 4) Fourier transformation are applied. In addition, operations between images, image overlay and principal component analysis are also applied.

  7. Support System for Information Extraction By Human • Method to designate particular facilities. • Method to mask some parts • Method to zoom in a target point

  8. Conclusion For areal-type damage various image processing tools are employed to detect damaged area. In this way, it is possible to find areal-type damage. However, for liner-type damage such as horizontal difference, it is comparatively difficult to detect damage. In this case, extraction by human should be applied and in order to help staff to extract, it is effective to overlay other data to focus on specific area/facility to check.

  9. QUERIES

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