In geographic data analysis, it is often the case that multiple aspects of a single phenomenon are captured by different sources of data. For instance, a storm can be identified based on its precipitation, as well as windspeed, and changes in barometric pressure. It proves beneficial in specific domains to be able to use all available sources of data, and some method must be used to integrate all of these sources of data into a singular decision, often in the form of a classification. This paper proposes the general form of what has been termed the Class Label Conflation Problem – the problem of taking a number of distinct and possibly conflicting sources in the form of spatially-located time series, and using this historical dataset to determine a class label at a new location. In addition to this formalization, this paper details an algorithm (called ACCL) to solve the general case of the problem. This algorithm has its foundations in information theory (specifically Dempster-Shafer Theory), supervised classification, and data fusion. An analysis of the algorithm demonstrates its effectiveness using synthetic datasets as well as the US Drought Monitor as a case study.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Saalfeld A (1988) Conflation automated map compilation. International Journal of Geographical Information System 2(3):217–228
Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Information Fusion 14(1):28–44
Quix C, Jarke M (2014) Information integration in research information systems. Procedia Computer Science 33:18–24
Foucher S, Germain M, Boucher J, Benie GB (2002) Multisource classification using ICM and Dempster-Shafer theory. IEEE Trans Instrum Meas 51(2):277–281
D. Kanungo and S. Sarkar, Multi-source land use land cover classification in a hilly terrain for landslide study, New Delhi, 2009
Le Hegarat-Mascle S, Bloch I, Vidal-Madjar D (1997) Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens 35(4):1018–1031
Sun P, Zhang Q, Wen Q, Singh VP, Shi P (2017) Multisource data-based integrated agricultural drought monitoring in the Huai River basin, China. Journal of Geophysical Research: Atmospheres 122(20):10751–10772
L.-K. Soh and C. Tsatsoulis, "multisource data and knowledge fusion for intelligent SAR Sea ice classification," in International Geoscience and Remote Sensing Symposium, 1999, 1999
Tsatsoulis C, Soh L-K, Bertoia C, Partington K (1999) "intelligent fusion of multisource data for sea ice classification," in Workshop on Intelligent Techniques for Spatio-Temporal Data Analysis in Environmental Applications. Chania, Greece
Chang Y-L, Liang L-S, Han C-C, Fang J-P, Liang W-Y, Chen K-S (2007) Multisource data fusion for landslide classification using generalized positive Boolean functions. IEEE Trans Geosci Remote Sens 45(6):1697–1708
P. F. Fisher, A. J. Comber and R. Wadsworth, "Land use and land cover: contradiction or complement.," in Re-presenting GIS, 2005, pp. 85–98
Grant DE (1979) Land-use/land-cover mapping from aerial photographs. Photogramm Eng Remote Sens 45(5):661–668
A. Srinivasan and J. A. Richards, "Knowledge-based techniques for multi-source classification," International Journal of Remote Sensing, vol. 11, no. 3, pp. 505–525, 1 March 1990
Amarsaikhan D, Douglas T (2004) Data fusion and multisource image classification. Int J Remote Sens 25:3529–3539
Yu S, Jia S, Xu C (2017) Convolutional neural networks for hyperspectral image classification. Neurocomputing 219:88–98
Ma X, Wang H, Wang J (2016) Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning. ISPRS J Photogramm Remote Sens 120:99–107
Ran YH, Li X, Lu L, Li ZY (2012) Large-scale land cover mapping with the integration of multi-source information based on the Dempster-Shafer theory. Int J Geogr Inf Sci 26(1):169–191
Rahmati O, Melesse AM (2016) Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran. Sci Total Environ 568:1110–1123
Al-Abadi AM (2017) The application of Dempster–Shafer theory of evidence for assessing groundwater vulnerability at Galal Badra basin, Wasit governorate, east of Iraq. Appl Water Sci 7(4):1725–1740
Hégarat-Mascle L, Richard D, Ottlé C (2003) Multi-scale data fusion using Dempster-Shafer evidence theory. Integrated Computer-Aided Engineering 10(1):9–22
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton
Dempster AP (1967) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38(2):325–339
Bentley JL (September 1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517
Gauss CF (1828) Disquisitiones Generales circa Superficies Curvas. Typis Dieterichianis, Göttingen
Harris SA (1973) Comments on the application of the Holdridge system for classification of world life zones as applied to Costa Rica. Arct Alp Res 5(3):A187–A191
"United States Drought Monitor," The National Drought Mitigation Center, 2017. [Online]. Available: http://droughtmonitor.unl.edu. [Accessed 12 12 2017]
"CoCoRaHS - Community Collaborative Rain, Hail & Snow Network," Colorado Climate Center, 2017. [Online]. Available: https://www.cocorahs.org/. [Accessed 1 3 2017]
USDA, NDMC, NCEI, NOAA, "United States Drought Monitor," 24 April 2014. [Online]. Available: https://www.ncdc.noaa.gov/sites/default/files/April-22-2014-US-Drought-Monitor-Map.png. [Accessed 13 January 2020]
Moon JT III, Guinan PE, Snider DJ, Lupo AR (2009) CoCoRaHs in Missouri: four years later, the importance of observations. Transactions of the Missouri Academy of Science 43:1–12
Reges HW, Doesken N, Turner J, Newman N, Bergantino A, Schwalbe Z (2016) CoCoRaHS: the evolution and accomplishments of a volunteer rain gauge network. Bull Am Meteorol Soc 97(10):1831–1846
Conflict of interest
The authors declare that they have no conflict of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Schell, Z., Samal, A. & Soh, LK. An information fusion approach for conflating labeled point-based time-series data. Geoinformatica 25, 1–41 (2021). https://doi.org/10.1007/s10707-020-00417-8
- Multi-source classification
- Spatial data fusion
- Dempster-Shafer theory
- Class label conflation
- Time series