% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Paris:1017987,
      author       = {Paris, Claudia and Martinez-Sanchez, Laura and van der
                      Velde, Marijn and Sharma, Surbhi and Sedona, Rocco and
                      Cavallaro, Gabriele},
      title        = {{A}ccuracy assessment of land-use-land-cover maps: the
                      semantic gap between in situ and satellite data},
      publisher    = {SPIE},
      reportid     = {FZJ-2023-04457},
      pages        = {14},
      year         = {2023},
      abstract     = {The availability of high-resolution, open, and free
                      satellite data has facilitated the production of global
                      Land-Use-Land-Cover (LULC) maps, which are extremely
                      important to monitor the Earth’s surface constantly.
                      However, generating these maps demands significant efforts
                      in collecting a vast amount of data to train the classifier
                      and to assess their accuracy. Although in-situ surveys are
                      generally regarded as reliable sources of information, it is
                      important to note that there may be inconsistencies between
                      the in-situ data and the information derived from satellite
                      data. This can be attributed to various factors (1)
                      differences in viewpoint perspectives, i.e., aerial versus
                      ground views, and (2) spatial resolution of the satellite
                      images versus the extent of the Land-Cover (LC) present in
                      the scene. The aim of this paper is to explore the
                      feasibility of using geo-referenced street-level imagery to
                      bridge the gap between information provided by field surveys
                      and satellite data. Unlike conventional in-situ surveys that
                      typically provide geo-tagged location-specific information
                      on LULC, street-level images offer a richer semantic context
                      for the sampling point under examination. This allows for
                      (1) an improved interpretation of LC characteristics, and
                      (2) a stronger correlation with satellite data. The
                      experimental analysis was conducted considering the 2018
                      Land Use and Coverage Area Frame Survey (LUCAS) in-situ
                      data, the LUCAS landscape (street-level) images and three
                      high-resolution thematic products derived from satellite
                      data, namely, Google’s Dynamic World, ESA’s World Cover,
                      and Esri’s Land Cover maps.},
      month         = {Sep},
      date          = {2023-09-03},
      organization  = {Image and Signal Processing for Remote
                       Sensing XXIX, Amsterdam (Netherlands),
                       3 Sep 2023 - 6 Sep 2023},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / ADMIRE - Adaptive
                      multi-tier intelligent data manager for Exascale (956748)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)956748},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001118768500018},
      doi          = {10.1117/12.2679433},
      url          = {https://juser.fz-juelich.de/record/1017987},
}