1Peter Bajcsy, 1Chulyun Kim, 2Jihua Wang and 2Yu-Feng Lin

1
A FRAMEWORK FOR GEOSPATIAL MODELING FROM SPARSE
FIELD MEASUREMENTS USING IMAGE PROCESSING AND
MACHINE LEARNING

• 1Peter Bajcsy, 1Chulyun Kim, 2Jihua Wang and
  2Yu-Feng Lin
• 1National Center for Supercomputing Applications
  (NCSA)
• 2Illinois State Water Survey (ISWS)
• University of Illinois at Urbana-Champaign (UIUC)

2
Outline

• Introduction
• Problems Addressed by Spatial Pattern To Learn
  (SP2Learn)
• SP2Learn Architecture and Functionality Overview
• Running SP2Learn
• Summary

3
Introduction
4
General Problem

• Compute a set of geo-spatially dense accurate
  predictions of variables
• given a set of direct geo-spatially sparse point
  measurements and
• auxiliary variables with implicit relationships
  with respect to the predicted variable
• Motivation
• minimize cost of taking direct point measurements
• maximize accuracy of predictions and
• automate discovering relationships among direct
  field measurements and indirect variables

5
Formulation

• Input sets of geo-spatially sparse variables
  Vipij dense auxiliary variables a priori
  tacit knowledge of experts
• Output geo-spatially dense (raster) Ok
• Unknown selection of methods workflow of
  operations/methods parameters of methods
  relationships of auxiliary variables w.r.t Ok
  quantitative metric of output goodness

p2j
Interpolations Mathematical models




p1j
V1 V2
O1
Auxiliary Variables Tacit Knowledge
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Applied Problem
Recharge and Discharge Rate Prediction
Bedrock elevation
Discharged
Recharged
Water table elevation
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Interdisciplinary Objectives

• Ground Water (Hydrologic Science) View
• Evaluation of Alternative Conceptual (implicit
  relationships) and Mathematical Models (explicit
  relationships)
• Accurate Prediction of Groundwater Recharge and
  Discharge Rates from Limited Number of Field
  Measurements
• Computer Science View
• Computer-Assisted Learning to Assess Alternative
  Conceptual and Mathematical Models
• Optimization of Prediction Models From a Set of
  Geo-Spatially Sparse Point Measurements

DIALOG
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State-of-the-Art Results

• Limited Spatial Resolution and Accuracy

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Existing Software for Groundwater and Surface
Water Modeling

• MODFLOW is a three-dimensional finite-difference
  ground-water model
• http//water.usgs.gov/nrp/gwsoftware/modflow2005/m
  odflow2005.html - freeware (2005)
• PEST - is software for model calibration,
  parameter estimation and predictive uncertainty
  analysis
• http//www.sspa.com/pest/ - freeware (2007)
  University of Queensland, Australia
• Precipitation-Runoff Modeling System (PRMS) is
  deterministic, distributed-parameter modeling
  system developed to evaluate the impacts of
  various combinations of precipitation, climate,
  and land use on streamflow, sediment yields, and
  general basin hydrology
• http//water.usgs.gov/software/prms.html -
  freeware (1996) USGS
• Deep Percolation Model (DPM) - facilitates
  estimation of ground-water recharge under a large
  range in climatic, landscape, and land-use and
  land-cover conditions
• http//pubs.usgs.gov/sir/2006/5318/ USGS

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Related Work

• Singh A. et al. Expert-Driven Perceptive
  Models for Reducing User Fatigue in an
  Interactive Hydrologic Model Calibration
  Framework

Conductivity (K) and Hydraulic heads (H) for the
hypothetical aquifer
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Motivation

• Ground Water (Hydrologic) Science
• Currently, there is no single method that could
  estimate R/D rates and patterns for all practical
  applications.
• Therefore, cross analyzing results from various
  estimation methods and related field information
  is likely to be superior than using only a single
  estimation method.
• Computer Science
• It is currently impossible
• (a) to replace an expert with a lot of tacit
  domain knowledge by computer algorithms or
• (b) to learn by an expert new I/O relationships
  from a plethora of possible variables and an
  extremely large space of processing methods and
  their parameters
• Thus, assisting experts to discover, evaluate
  and validate new relationships in an iterative
  way will likely enable
• (a) better understanding of the underlying
  phenomena, and
• (b) more automated and cost-efficient predictions

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Problems Addressed by Spatial Pattern To Learn
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Our Approach

• Data-Driven Analyses to Test Alternative Models,
  and to Search the Space of Processing Operations
  and Their Parameters
• Interpolation methods
• Mathematical models
• Image processing algorithms
• Machine learning algorithms
• Scalability of algorithms with large size data
• Computer-Assisted Comparisons and Evaluations of
  Multiple Models and Sub-Optimal Solutions
• Model/Solution Representation
• Closed Loop (Iterative) Workflows
• Human Computer Interfaces
• Overall Approach An Exploration Framework for a
  Class of Alternative Models/Hypotheses and
  Optimal Solutions

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SP2Learn Problem Formulation

• Given a set of geo-spatially sparse field
  measurements and auxiliary variables, derive
  accurate, spatially dense, R/D rate map by
• (a) using physics-based model
• (b) incorporating boundary conditions and
• (c) exploring auxiliary variables representing
  prior knowledge about R/D patterns but missing in
  the physics-based model

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Challenges

• (1) How to Recognize Meaningful Pattern of
  Predicted Map?
• (2) How to Quantify the Goodness of the Pattern?
• Approach
• (1a) Recognize patterns by utilizing multiple
  image enhancement and segmentation techniques
  applied to R/D rate predictions
• (1b) Introduce relationship between R/D pattern
  and auxiliary (a priori reference) information
• (2a) Define goodness w.r.t. reference information
  using experts selection of meaningful
  relationships
• (2b) Define goodness w.r.t. reference information
  using complexity of machine learning

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Using Physics-Based Model
R/D Rate Prediction
Field Measurements














Discharged
Recharged

Water table elevation
Hydraulic conductivity
Incoming water
Outgoing water
Bed rock elevation
Ground water fluxhydraulic conductivity cell
area gradient of water table elevation (head)
over cell distance
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Incorporating Spatial Boundary Conditions

• BC R/D rate prediction could have smooth
  transitions and recharge discharge regions
  (contiguous pixels) should be clearly delineated
• Approach Apply Image Restoration and De-noising
  Techniques
• Moving average based low pass filter
• TVL (Total Variation regularized L1-norm
  function) based filter
• Morphological operation based filter
• Using multiple techniques multiple times

Discharged
Recharged
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Exploring Auxiliary Variables Driving R/D Patterns
Prior Tacit Knowledge about R/D and Auxiliary
Variables

• Proximity to River P(R or D area/River is
  close)high

• Soil Type P(R or D area/SoilClay)low

• Slope P(R or D area/ slopehigh)low

moving average normalizationTVL
normalizationTVL
moving average
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From Auxiliary Variables To Knowledge and
Accurate R/D
Load Variables
Integrate Maps
Load R/D Map
Define ROI
Create Decision Tree
Apply Rules
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SP2Learn Output

• A set of rules that define relationships between
  predicted (R/D rate) variable and auxiliary
  variables
• Modified (more accurate) predictions according to
  the user selected rules defining relationships of
  predicted and auxiliary variables
• Sensitivity analysis results with respect to
• Methods (interpolations, image enhancement, )
• Models
• Parameters

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Example Results
ROI

• ltRULE ID138 NUM_OF_CASES3975 SUPPORT32.65gt
• ltIFgtElevation is not in 330-344 AND
• Soil type is in RmRoscommon muck AND
• Proximity to water body is not near_water AND
• Slope is in 0-0.9 lt/IFgt
• ltTHENgtR/D rate is -0.004,-0.002lt/THENgt

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SP2Learn Architecture and Functionality
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Underlying SP2Learn Technology
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SP2Learn Functionality Overview
Load Raster Step
Integration Step
Create Mask Step
Rules Step
Attribute Selection Step
Apply Rule Step
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SP2Learn Workflow
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On-Line Help
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Software and Test Data Download

• Download web page of Image Spatial Data Analysis
  group at NCSA http//isda.ncsa.uiuc.edu/download/

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Running SP2Learn
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Input Data to SP2Learn

• Raster files (maps)
• Predicted R/D rate models
• Auxiliary variables
• For mask creation
• Tables with geo-points
• Vector files with boundaries
• Raster files of categorical or continuous
  variables

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Image Processing

• Filtering Methods
• Low pass (moving average) filters
• Morphological filters
• TVL1 (Total Variation regularized L1 function)
• Using multiple techniques multiple times
• Parameters
• Kernel size (row dimension, column dimension)

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Example Input Maps
Morphological Opening
Morphological Closing
Low Pass Filter
Kernel (10,10)
Kernel (10,10)
Kernel (10,10)
Kernel (5,5)
Kernel (5,5)
Kernel (5,5)
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Example Auxiliary Maps

• Slope
• DEM
• Soil
• River Stream

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Loading Files

• Load R/D rate models (maps)
• Load auxiliary maps to explore alternative models
• Proximity to water
• Soil type
• Slope

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Mosaic Maps

• Large spatial coverage a set of tiles
• Out-of-core representation

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Viewing Images

• Right mouse click
• Image information
• Zoom
• Check boxes
• Pseudo-color
• Auto-fit images

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Registration

• Integration of all maps (raster images) to a
  common projection and spatial resolution

Before Convert
After Convert
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Create Mask
C
A
Mask Parameters
Visualization Panel
B
Mask Operations
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Mask Creation Options in SP2Learn
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User Defined Mask Creation

• Set Parameter User defined
• Mouse click-and-drag selection of region
• Click Paint and Show
• Click Apply

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Label Editor

• Assign categorical labels to colors

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Attribute Selection

• Output Predicted Variable
• Input Auxiliary Variables
• Check-boxes
• Show Table
• Prune Tree

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Decision Tree Based Modeling

• Tree structure can be represented as a set of
  rules

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Rules from Decision Tree

• Num Node number in a decision tree.
• Support() Among all cases satisfying
  conditions, the ratio of cases having the same
  class (conclusion).
• of cases The number of cases satisfying
  conditions
• Class Conclusion of a rule
• Conditions Conditions of a rule
• MDL Score MDL score of a decision tree. The less
  the score is, the better the tree is

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Show Decision Tree
Show Tree Option
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Export Rules

• XML format

Export Rules Option
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Apply Rules

• Visualization of
• Modified output variable
• Changed pixels
• Magnitude of changes (differences)

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Summary

• Novel Frameworks and Methodologies for
  Exploratory Data-Driven Modeling and Scientific
  Discoveries
• Problems addressed in the prototype SP2Learn
  solution
• Prediction accuracy improvement by a combination
  of mathematical models and data-driven (knowledge
  based) models, supervised and unsupervised
  iterative model optimization
• Better Data Utilization!

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Extra Information

• A stack of informatics and cyber-infrastructure
  software is open source
• Other software of potential interest
• GeoLearn is an exploratory framework for
  extracting information and knowledge from remote
  sensing imagery
• CyberIntegrator to support creation of
  exploratory workflows, reuse of workflows, remote
  server execution, data and process provenance
  tracking and analysis, streaming data support
• Image Provenance to Learn (IP2Learn) to support
  decision processes based on visual inspection of
  images
• Load Estimation (work in progress) to support
  optimal sampling of sediment loads using several
  sediment-discharge rating curves, bias correction
  factors and Monte Carlo simulations to predict
  confidence limits
• Download web page of Image Spatial Data Analysis
  group at NCSA http//isda.ncsa.uiuc.edu/download/
  

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Acknowledgement

• Funding Agencies
• NASA, NARA, NSF, NIH, NAVY, DARPA, ONR, NCSA
  Industrial Partners, NCSA Internal, COM UIUC,
  State of Illinois
• Full Time Employees
• Peter Bajcsy, Rob Kooper, Sang-Chul Lee, Luigi
  Marini
• Students
• Shadi Ashnai, Melvin Casares, Miles Johnson,
  Chulyun Kim, Qi Li, Tim Nee, Arlex Torres, Ryo
  Kondo, Henrik Lomotan, James Rapp
• Collaborators
• College of Applied Health Sciences UIUC,
  Kinesiology Dept. UIUC, CEE UIUC, CS UIUC, GISLIS
  UIUC
• UIC, UC Berkeley, Univ. of Texas at Austin, Univ.
  of Iowa
• ISWS, NARA, Nielsen, State Farm
• Instituto Tecnológico de Costa Rica, UNESCO-IHE
  Netherlands

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Thank you!

• Questions
• Peter Bajcsy pbajcsy_at_ncsa.uiuc.edu
• Need More Details
• Publications http//isda.ncsa.uiuc.edu

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