Reducing Uncertainty and Improving Precision in Coincident Geospatial Datasets Using Weight-of-Evidence: Part 1

Abstract

This U.S. Army Engineer Research and Development Center (ERDC) Technical Note (TN) is the first of multiple TNs focused on improving environmental datasets in limited-knowledge conditions by merging multiple datasets, each with high uncertainty and low precision, together with institutionalized subject matter expert knowledge to increase accuracy and precision. This TN provides a brief overview of geospatial data fusion and uncertainty quantification for environmental datasets. Additionally, this TN details the progress and current results following an investigation of the working hypothesis that a weight-of-evidence (WOE) framework that joins qualitative and quantitative datasets can significantly improve the accuracy and precision as related to individual datasets and current data fusion algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2019
Accession Number
AD1078595

Entities

People

  • Drew A. Loney
  • Igor Linkov
  • Jeffrey Cegan
  • Matthew Wood

Organizations

  • Engineer Research and Development Center

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Calibration
  • Case Studies
  • Data Fusion
  • Databases
  • Fuzzy Logic
  • Fuzzy Sets
  • Geographic Information Systems
  • Geographic Regions
  • Information Science
  • Information Systems
  • Judgment
  • Measurement
  • Precision
  • Remote Sensing
  • Risk Analysis
  • Standards

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Systems Analysis and Design
  • Wetland-Land-Environmental Management.