Definition
Geospatial data conflation is the compilation or reconciliation of two different geospatial datasets covering overlapping regions (Saalfeld 1988). In general, the goal of conflation is to combine the best quality elements of both datasets to create a composite dataset that is better than either of them. The consolidated dataset can then provide additional information that cannot be gathered from any single dataset.
Based on the types of geospatial datasets dealt with, the conflation technologies can be categorized into the following three groups:
-
Vector to vector data conflation: A typical example is the conflation of two road networks of different accuracy levels. Figure 1shows a concrete example to produce a superior dataset by integrating two road vector datasets: road network from US Census TIGER/Line files, and road network from the department of...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agouris P, Stefanidis A, Gyftakis S (2001) Differential snakes for change detection in road segments. Photogramm Eng Remote Sens 67(12):1391–1399
Chen C-C, Knoblock CA, Shahabi C, Chiang Y-Y, Thakkar S (2004a) Automatically and accurately conflating orthoimagery and street maps. In: Proceedings of the 12th ACM international symposium on advances in geographic information systems, Washington, DC
Chen C-C, Shahabi C, Knoblock CA (2004b) Utilizing road network data for automatic identification of road intersections from high resolution color orthoimagery. In: Proceedings of the second workshop on spatiotemporal database management (co-located with VLDB2004), Toronto
Chen C-C, Knoblock CA, Shahabi C (2006a) Automatically conflating road vector data with orthoimagery. Geoinformatica 10(4):495–530
Chen C-C, Shahabi C, Knoblock CA, Kolahdouzan M (2006b) Automatically and efficiently matching road networks with spatial attributes in unknown geometry systems. In: Proceedings of the third workshop on spatiotemporal database management (co-located with VLDB2006), Seoul
Cobb M, Chung MJ, Miller V, Foley H III, Petry FE, Shaw KB (1998) A rule-based approach for the conflation of attributed vector data. GeoInformatica 2(1):7–35
Dare P, Dowman I (2000) A new approach to automatic feature based registration of SAR and SPOT images. Int Arch Photogramm Remote Sens 33(B2):125–130
Eidenbenz C, Kaser C, Baltsavias E (2000) ATOMI – automated reconstruction of topographic objects from aerial images using vectorized map information. Int Arch Photogramm Remote Sens 33(Part 3/1):462–471
Filin S, Doytsher Y (2000) A linear conflation approach for the integration of photogrammetric information and GIS data. Int Arch Photogramm Remote Sens 33:282–288
Flavie M, Fortier A, Ziou D, Armenakis C, Wang S (2000) Automated updating of road information from aerial images. In: Proceedings of American society photogrammetry and remote sensing conference, Amsterdam
Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1(4): 321–331
Saalfeld A (1988) Conflation: automated map compilation. Int J Geogr Inf Sci 2(3): 217–228
Seedahmed G, Martucci L (2002) Automated image registration using geometrical invariant parameter space clustering (GIPSC). In: Proceedings of the photogrammetric computer vision, Graz
Walter V, Fritsch D (1999) Matching spatial data sets: a statistical approach. Int J Geogr Inf Sci 5(1): 445–473
Ware JM, Jones CB (1998) Matching and aligning features in overlayed coverages. In: Proceedings of the 6th ACM symposium on geographic information systems, Washington, DC
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this entry
Cite this entry
Chen, CC., Knoblock, C.A. (2017). Conflation of Geospatial Data. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_182
Download citation
DOI: https://doi.org/10.1007/978-3-319-17885-1_182
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17884-4
Online ISBN: 978-3-319-17885-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering