<p>This is&nbsp;<span style="font-family: Verdana, Arial, Helvetica, sans-serif;">Charlotte street netowrk data.</span></p>

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Cloud-free imageries, acquired from Landsat 8 OLI during 2016 to 2018, were used to delineate the extents of the glacial lakes in the mountainous terrain of CPEC

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Emergency  managers  of  today  grapple  with  post-hurricane damage assessment that is often labor-intensive, slow,costly,   and   error-prone.   As   an   important   first   step   towards addressing  the   challenge,   this   paper   presents   the   development of  benchmark  datasets  to  enable  the  automatic  detection  ofdamaged buildings from post-hurricane remote sensing imagerytaken  from  both  airborne  and  satellite  sensors.  Our  work  has two  major  contributions:  (1)  we  propose  a  scalable  framework to  create  benchmark  datasets  of  hurricane-damaged  buildings

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Data can be used for object detection algorithms to properly annotate post disaster buildings as either damaged or non damaged aiding disaster response. This dataset contains ESRI Shapefiles of bounding boxes of buildings labeled as either non-damaged or damaged. Those labeled as damaged also have four degrees of damage from minor to catastrophic. Importantly, each bounding box is also indexed to one of the images in the NOAA post Harvey hurricane imagery dataset allowing users to match the bounding boxes with the correct imagery for training the algorithm.

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