remote sensing

Dataset of rosbags collected during autonomous drone flight inside a warehouse of stockpiles. PCD files created using reconstruction method proposed by article.

Data still being move to IEEE-dataport. 

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The data relates to a study to captured deciduous broadleaf Bidirectional Scattering Distribution Functions (BSDFs) from the visible through shortwave-infrared (SWIR) spectral regions (350-2500 nm) and accurately modeled the BSDF for extension to any illumination angle, viewing zenith, or azimuthal angle. Measurements were made from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra).

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The Dataset

The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.

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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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In tropical/subtropical regions, the favorable climate associated with the use of agricultural technologies, such as no-tillage, minimum cultivation, irrigation, early varieties, desiccants, flowering inducing and crop rotation, makes agriculture highly dynamic. In this paper, we present the Campo Verde agricultural database. The purpose of creating and sharing these data is to foster advancement of remote sensing technology in areas of tropical agriculture, primarily the development and testing of methods for crop recognition and agricultural mapping.

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