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 {Vi{pij}} & 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

6. Applied Problem Recharge and Discharge Rate Prediction Discharged
Bedrock elevation
Discharged
Recharged
Water table elevation

7. 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

8. State-of-the-Art Results
Limited Spatial Resolution and Accuracy
Min. Grid:
805mX805m
Recharge zone
Noisy pattern or weak R/D
Discharge zone
Discharge
Recharge
Uniform Grid:
80mX80m

9. Existing Software for Groundwater and Surface Water Modeling
MODFLOW is a three-dimensional finite-difference ground-water model
- freeware (2005)
PEST - is software for model calibration, parameter estimation and predictive uncertainty analysis
- 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
- 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
USGS

10. 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

11. Motivation Ground Water (Hydrologic) Science: Computer 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

12. Problems Addressed by Spatial Pattern To Learn

13. 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

14. 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

15. 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 expert’s selection of ‘meaningful’ relationships
(2b) Define goodness w.r.t. reference information using complexity of machine learning

16. 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 flux=hydraulic conductivity * cell area * gradient of water table elevation (head) over cell distance

17. 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

18. Exploring Auxiliary Variables Driving R/D Patterns
Prior Tacit Knowledge about R/D and Auxiliary Variables
Soil Type: P(R or D area/Soil=Clay)~low
Slope: P(R or D area/ slope=high)~low
Proximity to River: P(R or D area/River is close)~high
moving average
normalization+TVL
normalization+TVL
moving average

19. From Auxiliary Variables To Knowledge and Accurate R/D
Load R/D Map
Load Variables
Integrate Maps
Apply Rules
Create
Decision Tree
Define ROI

20. 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

21. Example Results <RULE ID=138 NUM_OF_CASES=3975 SUPPORT=32.65%>
ROI
<RULE ID=138 NUM_OF_CASES=3975 SUPPORT=32.65%>
<IF>Elevation is not in { } AND
Soil type is in {Rm=Roscommon muck} AND
Proximity to water body is not {near_water} AND
Slope is in {0-0.9} </IF>
<THEN>R/D rate is ,-0.002</THEN> = +

22. SP2Learn Architecture and Functionality

23. Underlying SP2Learn Technology

24. SP2Learn Functionality Overview
Load Raster
Step
Integration
Step
Create Mask
Step
Rules
Step
Attribute
Selection
Step
Apply Rule
Step

25. SP2Learn Workflow

26. On-Line Help

27. Software and Test Data Download
Download web page of Image Spatial Data Analysis group at NCSA:

28. Running SP2Learn

29. Input Data to SP2Learn Raster files (maps) For mask creation
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

30. Image Processing Filtering Methods Parameters
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)

31. Example Input Maps Low Pass Filter Morphological Closing
Morphological Opening
Kernel = (10,10)
Kernel = (10,10)
Kernel = (10,10)
Kernel = (5,5)
Kernel = (5,5)
Kernel = (5,5)

32. Example Auxiliary Maps
Slope
DEM
Soil
River Stream

33. Loading Files Load R/D rate models (maps)
Load auxiliary maps to explore alternative models
Proximity to water
Soil type
Slope …

34. Mosaic Maps Large spatial coverage – a set of tiles
Out-of-core representation

35. Viewing Images Right mouse click Check boxes Image information Zoom
Pseudo-color
Auto-fit images

36. Registration
Integration of all maps (raster images) to a common projection and spatial resolution
Before “Convert”
After “Convert”

37. Create Mask C A
Mask Parameters
Visualization Panel B
Mask Operations

38. Mask Creation Options in SP2Learn

39. User Defined Mask Creation
Set Parameter: User defined
Mouse click-and-drag selection of region
Click Paint and Show
Click Apply

40. Label Editor
Assign categorical labels to colors

41. Attribute Selection Output: Predicted Variable
Input: Auxiliary Variables
Check-boxes
Show Table
Prune Tree

42. Decision Tree Based Modeling
Tree structure can be represented as a set of rules
Discharge
yes no
Recharge
Case A..
Case E..
Case J..
Distance from river ≤ 100 ft?
Soil Type is {sand}?

43. 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

44. Show Decision Tree
Show Tree Option

45. Export Rules
XML format
Export Rules Option

46. Apply Rules Visualization of Modified output variable Changed pixels
Magnitude of changes (differences)

47. 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!

48. 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:

49. Acknowledgement Funding Agencies: Full Time Employees: Students:
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

50. Thank you! Questions: Need More Details
Peter Bajcsy
Need More Details
Publications:

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