An information fusion approach for conflating labeled point-based time-series data

Zion Schell, Ashok Samal, Leen Kiat Soh

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1-41
Number of pages41
Issue number1
StatePublished - Jan 2021
Externally publishedYes


  • Belief
  • Class label conflation
  • Dempster-Shafer theory
  • Multi-source classification
  • Spatial data fusion
  • Time series

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Information Systems


Dive into the research topics of 'An information fusion approach for conflating labeled point-based time-series data'. Together they form a unique fingerprint.

Cite this