We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels); it is varied, covering 16 conurbations in France, with variousclimates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics.

  • Artificial Intelligence
  • Last Updated On: 
    Wed, 06/24/2020 - 04:26

    The Dataset

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

  • Computer Vision
  • Last Updated On: 
    Sat, 06/06/2020 - 12:11

    This database is a image set of a strongest glint-affected region of inland water Capivara reservoir, Brazil. We carried out a flight survey in September 2016 on the confluence region of the Tibagi and Paranapanema Rivers. We use the hyperspectral camera manufactured by Rikola, model FPI2014, wich collect 25 spectral bands at following intervals and full widths at half maximum (FWHM), both expressed in nanometers (nm):


  • Remote Sensing
  • Last Updated On: 
    Tue, 11/12/2019 - 10:34

    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

  • Remote Sensing
  • Last Updated On: 
    Wed, 12/04/2019 - 09:51