III-CXT-Small: Collaborative Research: Automatic Geomorphic Mapping and Analysis of Land Surfaces Using Pattern Recognition

Information

  • NSF Award
  • 0812271
Owner
  • Award Id
    0812271
  • Award Effective Date
    9/1/2008 - 16 years ago
  • Award Expiration Date
    4/30/2011 - 13 years ago
  • Award Amount
    $ 284,871.00
  • Award Instrument
    Standard Grant

III-CXT-Small: Collaborative Research: Automatic Geomorphic Mapping and Analysis of Land Surfaces Using Pattern Recognition

Description<br/><br/>Advances in remote sensing techniques have made available large datasets of topographic measurements pertaining to terrestrial and planetary land surfaces. However, the scientific utilization of these datasets is hampered by a lack of tools for effective automated analysis. This project seeks to develop a system for fast, objective and transparent conversion of topographic data into knowledge about land surfaces. The project has two complementary goals: 1) to develop a tool that autonomously produces geomorphic maps mimicking traditional, manually derived maps in their appearance and content, and 2) to develop a tool that classifies entire topographic scenes into characteristic landscape categories. The mapping tool is based on the object-oriented supervised classification principle. A number of novel solutions, including semi-supervised learning, meta-learning, and a wrapping technique coupling classification and segmentation, are proposed to address challenges posed by the specificity of topographic data. The scene classification tool is based on information-theoretic metrics and incorporates novel solutions to problems posed by the raster character of topographic datasets.<br/><br/>Intellectual Merit<br/><br/>The project employs a novel fusion of machine learning and computer vision techniques to open new possibilities. In the process of constructing the mapping and classifying tools, novel machine learning methodologies will be developed and tested. The products of this research will enable a qualitatively new type of analysis of land surface topography: the large scale statistical comparison of spatial distribution of landforms.<br/><br/>Broad Impact<br/><br/>Successful mapping and classifying tools will have impact beyond the analysis of natural landscapes; they can be also be applied to the study of surface metrology (the numerical characterization of industrial surfaces). The nature of this project will attract interest and collaboration with specialists from diverse disciplines, such as computer science, remote sensing, geomorphology and hydrology. Such links will broaden the base of expertise for each discipline, as well as enrich participants from contributing domains.

  • Program Officer
    Maria Zemankova
  • Min Amd Letter Date
    8/15/2008 - 16 years ago
  • Max Amd Letter Date
    8/15/2008 - 16 years ago
  • ARRA Amount

Institutions

  • Name
    Universities Space Research Association
  • City
    Columbia
  • State
    MD
  • Country
    United States
  • Address
    7178 Columbia Gateway Drive
  • Postal Code
    210462581
  • Phone Number
    4107302656

Investigators

  • First Name
    Tomasz
  • Last Name
    Stepinski
  • Email Address
    stepintz@uc.edu
  • Start Date
    8/15/2008 12:00:00 AM

FOA Information

  • Name
    Information Systems
  • Code
    104000

Program Element

  • Text
    INFO INTEGRATION & INFORMATICS
  • Code
    7364

Program Reference

  • Text
    INFO INTEGRATION & INFORMATICS
  • Code
    7364
  • Text
    ADVANCED SOFTWARE TECH & ALGOR
  • Code
    9216
  • Text
    HIGH PERFORMANCE COMPUTING & COMM