499
Views
6
CrossRef citations to date
0
Altmetric
Research Articles

SOCO-Field: observation capability representation for GeoTask-oriented multi-sensor planning cognition

, , , &
Pages 205-228 | Received 14 Dec 2018, Accepted 11 Aug 2019, Published online: 22 Aug 2019

References

  • Alamdar, F., Kalantari, M., and Rajabifard, A., 2015. An evaluation of integrating multisourced sensors for disaster management. International Journal of Digital Earth, 8 (9), 727–749. doi:10.1080/17538947.2014.927537
  • Bian, L., 2007. Object‐oriented representation of environmental phenomena: is everything best represented as an object? Annals of the Association of American Geographers, 97 (2), 267–281. doi:10.1111/j.1467-8306.2007.00535.x
  • Botts, M., 2014. OGC® sensorml: model and XML encoding standard. Technical report OGC 12-000, Open Geospatial Consortium.
  • Botts, M. and Robin, A., 2007. OpenGIS sensor model language (SensorML) implementation specification. Technical report OGC 07-000, Open Geospatial Consortium.
  • Chen, C. and Helal, A., 2009. Device integration in SODA using the device description language. In: P. Kellenberger, ed. Ninth international symposium on applications and the internet, 20–24 July 2009. Bellevue, Washington, USA. Washington: IEEE Computer Society, 100–106.
  • Chen, C. and Helal, S., 2008. Sifting through the jungle of sensor standards. IEEE Pervasive Computing, 7 (4), 84–88. doi:10.1109/MPRV.2008.81
  • Chen, F. and Liu, C., 2012. Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy and Water Environment, 10 (3), 209–222. doi:10.1007/s10333-012-0319-1
  • Chen, Z., et al., 2012. Cloud computing enabled web processing service for earth observation data processing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (6), 1637–1649. doi:10.1109/JSTARS.2012.2205372
  • Couclelis, H., 1992. People manipulate objects (but cultivate fields): beyond the raster-vector debate in GIS. In: A.U. Frank, I. Campari, and U. Formentini, eds. Theories & methods of spatio-temporal reasoning in Geographic Space, 21–23 September 1992 Italy. Berlin: Springer-Verlag, 65–77.
  • Cova, J. and Goodchild, M., 2002. Extending geographical representations to include fields of spatial objects. International Journal of Geographical Information Science, 16, 509–532. doi:10.1080/13658810210137040
  • Fan, H., et al., 2015. Capability representation model for heterogeneous remote sensing sensors: case study on soil moisture monitoring. Environmental Modelling & Software, 70, 65–79. doi:10.1016/j.envsoft.2015.04.005
  • Goodchild, M.F., 1992. Geographical data modeling. Computers & Geosciences, 18 (4), 401–408. doi:10.1016/0098-3004(92)90069-4
  • Goodchild, M.F., et al., 1999. Introduction to the Varenius project. International Journal of Geographical Information Science, 13, 731–745. doi:10.1080/136588199240996
  • Goodchild, M.F., et al., 2012. Next-generation digital earth. Proceedings of the National Academy of Sciences, 109 (28), 11088–11094. doi:10.1073/pnas.1202383109
  • Goodchild, M.F., Yuan, M., and Cova, T.J., 2007. Towards a general theory of geographic representation in GIS. International Journal of Geographical Information Science, 21, 239–260. doi:10.1080/13658810600965271
  • Hu, C., Chen, N., and Li, J., 2013. Geospatial web-based sensor information model for integrating satellite observation. Photogrammetric Engineering & Remote Sensing, 79 (10), 915–927. doi:10.14358/PERS.79.10.915
  • Hu, C., et al., 2014. An observation capability metadata model for EO sensor discovery in sensor web enablement environments. Remote Sensing, 6 (11), 10546–10570. doi:10.3390/rs61110546
  • Hu, C., et al., 2016. Representing geospatial environment observation capability information: a case study of managing flood monitoring sensors in the Jinsha river basin. Sensors, 16 (12), 2144. doi:10.3390/s16122100
  • Kang, L., 2000. IEEE 1451: a standard in support of smart transducer networking. In: Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference, 1–4 May 2000 Hilton Hotel and Towers, Baltimore, Maryland, U.S.A. Washington: IEEE Computer Society, 525–528.
  • Kang, L., 2007. Sensor standards harmonization-path to achiewving sensor interoperability. In: 2007 Autotestcon IEEE, 17–20 September 2007 Baltimore, Maryland. Washington: IEEE Computer Society, 381–388.
  • Kjenstad, K., 2006. On the integration of object‐based models and field‐based models in gis. International Journal of Geographical Information Science, 20 (5), 491–509. doi:10.1080/13658810600607329
  • Kussul, N. and Skakun, S., 2014. Resilience aspects in the sensor web infrastructure for natural disaster monitoring and risk assessment based on earth observation data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (9), 3826–3832. doi:10.1109/JSTARS.2014.2313573
  • Li, D., et al., 2017. Earth observation brain (EOB): an intelligent earth observation system. Geo-spatial Information Science, 20 (2), 134–140. doi:10.1080/10095020.2017.1329314
  • Li, D., et al., 2014. From digital earth to smart earth. Chinese Science Bulletin, 59 (8), 722–733. doi:10.1007/s11434-013-0100-x
  • Liang, S. and Huang, C., 2013. GeoCENS: a geospatial cyberinfrastructure for the world-wide sensor web. Sensors, 13 (10), 13402–13424. doi:10.3390/s131013402
  • Liu, Y., et al., 2008. Towards a general field model and its order in GIS. International Journal of Geographical Information Science, 22 (6), 623–643. doi:10.1080/13658810701587727
  • Matsumoto, S., 2010. Echonet: a home network standard. IEEE Pervasive Computing, 9 (3), 88–92. doi:10.1109/MPRV.2010.57
  • Peuquet, D., 1988. Toward a conceptual synthesis. Annals of the Association of American Geographers, 78 (3), 375–394. doi:10.1111/j.1467-8306.1988.tb00214.x
  • Rolf, A. and de By, R.A., 2015. Principles of geographic information systems. Oxford: Oxford university press.
  • Römer, H., et al., 2014. Airborne near-real-time monitoring of assembly and parking areas in case of large-scale public events and natural disasters. International Journal of Geographical Information Science, 28 (4), 682–699. doi:10.1080/13658816.2013.866240
  • Sun, Z., et al., 2012. A task ontology driven approach for live geoprocessing in a service-oriented environment. Transactions in GIS, 16 (6), 867–884. doi:10.1111/tgis.2012.16.issue-6
  • Umer, M., Kulik, L., and Tanin, E., 2010. Spatial interpolation in wireless sensor networks: localized algorithms for variogram modelling and Kriging. GeoInfomatica, 14 (1), 101–134. doi:10.1007/s10707-009-0078-3
  • Voudouris, V., 2010. Towards a unifying formalisation of geographic representation: the object-field model with uncertainty and semantics. International Journal of Geographical Information Science, 24 (12), 1811–1828. doi:10.1080/13658816.2010.488237
  • Voudouris, V., Wood, J., and Fisher, P., 2005. Collaborative geoVisualization: object-field representations with semantic and uncertainty information. In OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”, 31 October-4 November 2005 Agia Napa, Cyprus. Berlin, Heidelberg: Springer, 1056–1065.
  • Wang, F. and Yuan, H., 2010. Challenges of the sensor web for disaster management. International Journal of Digital Earth, 3 (3), 260–279. doi:10.1080/17538947.2010.484510
  • Wang, X., Wang, S., and Ma, J.J., 2007. An improved co-evolutionary particle swarm optimization for wireless sensor networks with dynamic deployment. Sensors, 7 (3), 354–370. doi:10.3390/s7030354
  • Worboys, M.F. and Duckham, M., 1995. GIS: a computing perspective. London: Taylor and Francis.
  • Yuan, M., 1999. Representing geographic information to enhance GIS support for complex spatiotemporal queries. Transactions in GIS, 3, 137–160. doi:10.1111/1467-9671.00012
  • Zhang, S., et al., 2016. Robust estimation for light field via spinning parallelogram operator. Computer Vision and Image Understanding, 145, 148–159. doi:10.1016/j.cviu.2015.12.007
  • Zhang, X., et al., 2018. Geospatial sensor web: a cyber-physical infrastructure for geoscience research and application. Earth-Science Reviews, 684–730. doi:10.1016/j.earscirev.2018.07.006

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.