@article{fdi:010062904,
  title = {{F}ormalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation},
  author = {{P}ierkot, {C}hristelle and {A}ndr{\'e}s, {S}. and {F}aure, {J}ean-{F}ran{\c{c}}ois and {S}eyler, {F}r{\'e}d{\'e}rique},
  editor = {},
  language = {{ENG}},
  abstract = {{T}echnological tools allow the generation of large volumes of data. {F}or example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines, such as urban planning, environmental sciences, and health care. {T}hus, remote-sensing experts must handle various and complex image sets for their interpretations. {T}he {GIS} community has undertaken significant work in describing spatiotemporal features, and standard specifications nowadays provide design foundations for {GIS} software and spatial databases. {W}e argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. {A}s a result, we propose a high level conceptual framework, based on existing and standardized approaches, offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge.},
  keywords = {{TELEDETECTION} {SPATIALE} ; {IMAGE} {SATELLITE} ; {INTERPRETATION} {D}'{IMAGE} ; {MODELISATION} ; {METHODOLOGIE} ; {SEMANTIQUE} ; {ETUDE} {DE} {CAS} ; {COUVERT} {VEGETAL} ; {VARIATION} {SPATIALE} ; {VARIATION} {TEMPORELLE} ; {ONTOLOGIE} ; {REPRESENTATION} {DES} {CONNAISSANCES} ; {STANDARDISATION} ; {AMAZONIE} ; {BRESIL} ; {PARA} {BRESIL} ; {SANTAREM}},
  booktitle = {},
  journal = {{J}ournal of {S}patial {I}nformation {S}cience},
  numero = {7},
  pages = {77--98},
  ISSN = {1948-660{X}},
  year = {2013},
  DOI = {10.5311/{JOSIS}.2013.7.142},
  URL = {https://www.documentation.ird.fr/hor/fdi:010062904},
}