@article{fdi:010068289,
  title = {{A} look inside the {P}lant{N}et experience},
  author = {{J}oly, {A}. and {B}onnet, {P}. and {G}oeau, {H}. and {B}arbe, {J}. and {S}elmi, {S}. and {C}hamp, {J}. and {D}ufour-{K}owalski, {S}. and {A}ffouard, {A}ntoine and {C}arre, {J}. and {M}olino, {J}ean-{F}ran{\c{c}}ois and {B}oujemaa, {N}. and {B}arth{\'e}lemy, {D}.},
  editor = {},
  language = {{ENG}},
  abstract = {{P}l@nt{N}et is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. {O}ne year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. {W}e first demonstrate the attractiveness of the developed multimedia system (with more than 90{K} end-users) and the nice self-improving capacities of the whole collaborative workflow. {W}e then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. {W}e discuss in particular two main issues: the bias and the incompleteness of the produced data. {W}e finally open new perspectives and describe upcoming realizations towards bridging these gaps.},
  keywords = {},
  booktitle = {{M}ultimedia in ecology},
  journal = {{M}ultimedia {S}ystems},
  volume = {22},
  numero = {6 (no sp{\'e}cial)},
  pages = {751--766},
  ISSN = {0942-4962},
  year = {2016},
  DOI = {10.1007/s00530-015-0462-9},
  URL = {https://www.documentation.ird.fr/hor/fdi:010068289},
}