National Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-Champaign

Geospatial Informatics:

From Raw Data To Information and To KnowledgePresenters:Peter Bajcsy, Michal Ondrejcek, Rob Kooper and Luigi MariniNCSA/UIUC

Overview of Geospatial Informatics Presentations at the ILGISA Workshop

Imaginations unbound

Outline• Background to Geospatial Informatics • Introduction to Geospatial Informatics • Prediction Modeling from Spatially Sparse Data

• A Framework for Computer-Assisted Learning of Spatially Dense Recharge/Discharge Rate Models

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

• Other Similar Solutions• An Exploratory Framework for Extracting Information and Knowledge

from Remote Sensing Imagery• Integration of Ground and Remote Sensing Variables for Better

Understanding of Algal Biomass Bloom in Illinois

• Summary and Open Problems

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Background to Geospatial Informatics

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Background• Funded collaborations in the interdisciplinary areas related

to hydrology, environmental engineering, water quality, ground water and computer science:• ISWS: Momcilo Markus, Yu-Feng Lin• CEE UIUC: Praveen Kumar, Barbara Minsker • UNESCO-IHE Netherlands, Instituto Tecnológico de Costa Rica,

• On-going efforts:• The current collaborations and many other preliminary collaborations

have led to proposals and are currently pending.• NCSA Petascale proposal to NSF

• Terra/Peta/Exa/Zetta/Yotta-scale Computing Will Require Large Investments

• Many Times Large Investments Have to Be Driven by Large Impact Application Drivers

• Institute for Advanced Computation and Technology (IACAT): Advanced Information Theme

• NSF funded efforts to build Earth Observatories

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Earth Observatories• Multiple efforts to build Earth

Observatories to better understand • our environment in general• the principles governing changes in nature• the decision making required in order to

address the urban and rural environment sustainability challenges.

• Example National Efforts in US: • WATERS, CUAHSI, CLEANER, NEES,

GEON • NEON, ORION, LOOKING, LTER, CZEN,

…

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WATERS

CUAHSI

Earth Observatories: Challenges • Task: Design, Implement, Test, Deploy, Expand

and Maintain Earth Observatories• Challenges:

• It cannot be done without a multi-disciplinary effort• It is based on rapidly changing information technology

stacks• It has to scale across multiple geographic sites and

continents• It is being built based on community consensus which

has to be reached at some level to make any progress • How can we help building Earth Observatories?

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Introduction to Geospatial Informatics

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Geospatial Informatics: Overview

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Common Requirements• Data:

• Access large amounts of hydrologic, geographic, meteorological, water quality, soil type, land-use and many other types of data

• Ingest and integrate heterogeneous large size data and streaming data• Computational Resources:

• Perform complex CPU and memory intensive data-driven analyses• Utilize a spectrum of distributed computational resources

• Data-driven Analyses (Software):• Design data-driven (data mining, machine learning, pattern recognition,

statistical) analyses• Visualize and interpret data-driven models• Integrate data-driven models with physics/chemistry/bio based models

• Data and Software Integration:• Exercise seamlessly functionality present in heterogeneous software

packages using available computational resources• Provide an environment where heterogeneous visualization and mining

tools could be integrated into workflows, and the analysis workflows could be re-used and modified.

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X-informatics Problems• X-informatics Problems: Given some hardware,

software and data, extract information and knowledge from data

• X-informatics Objectives: • Analytical capabilities

• Data-driven analyses, machine learning, pattern recognition, modeling

• Automation• Integration of data and software,

computational scalability, provenance gathering

• Learning• Scientific discovery by visual exploration,

decision support, sharing and collaboration

Terminology

• Raw data: “Anything that can be ingested by a computer”

• Information: “Meaning of raw data that is narrow in scope and it has a simple organization”

• Knowledge: “Interpretation of information that is broad in scope and it is orderly synthesized”

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Terminology• Informatics (or information science):

• “The process of going from raw data/measurements to information and knowledge”

• X-Informatics:• “Informatics with the focus on inter-

disciplinary research and development of information technology that bridges boundaries across hardware, software and domain users”

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Why X-Informatics?

• Plethora of end-to-end engineering efforts in many scientific disciplines, where X-informatics is the key component

X-informatics Approaches

• Kumar P., J. Alameda, P. Bajcsy, M. Folk and M. Markus, “Hydroinformatics : Data Integrative Approaches in Computation, Analysis, and Modeling,” CRC Press LLC, 2006.

• Bajcsy P., “A Perspective on Cyberinfrastructure for Water Research Driven by Informatics Methodologies,” Geography Compass, Volume 2, Issue 6 (p 2040-2061), 2008 Blackwell Publishing Ltd

• Bajcsy P., R. Kooper, L. Marini and J. Myers, “Community-Scale Cyberinfrastructure for Exploratory Science,” In: Cyberinfrastructure Technologies and Applications book, Editor: Junwei Cao, Nova Science Publishers, Inc., Chapter 12, 1st quarter of 2009,

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Prediction Modeling from Spatially Sparse Data

-- Spatial Pattern To Learn Framework

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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

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

p1j

Interpolations

Mathematical models

p2j

V1 & V2 O1Auxiliary Variables & Tacit Knowledge

Applied Problem

Discharged Recharged

Recharge and DischargeRate Prediction

Bedrock elevation

Water table elevation

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 MeasurementsDIALOG

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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

• MODFLOW is a three-dimensional finite-difference ground-water model • http://water.usgs.gov/nrp/gwsoftware/modflow2005/modflow2005.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

Existing Software for Groundwater and Surface Water Modeling

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

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

Problems Addressed by Spatial Pattern To Learn

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

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

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

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

Using Physics-Based Model

Incoming water Outgoing waterBed rock

elevation +

Water table elevation +

Hydraulic conductivity +

Discharged Recharged

Field MeasurementsR/D Rate Prediction

+ + ++++ +

+

+

+

++

+

++

* * dhQ K AdL

=

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

Exploring Auxiliary Variables Driving R/D Patterns

moving average normalization+TVL moving averagenormalization+TVL

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

Soil Type: P(R or D area/Soil=Clay)~low

Slope: P(R or D area/ slope=high)~low

Prior Tacit Knowledge about R/D and Auxiliary Variables

From Auxiliary Variables To Knowledge and Accurate R/D

Load Variables Integrate Maps

Define ROIMap Ranking

Load R/D Map

Learn from Data-Driven 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

Example Results

• <RULE ID=138 NUM_OF_CASES=3975 SUPPORT=32.65%>• <IF>Elevation is not in {330-344} 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 -0.004,-0.002</THEN>

= +

ROI

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SP2Learn Architecture and Functionality

Underlying SP2Learn Technology

SP2Learn Functionality Overview

Load RasterStep

IntegrationStep

RulesStep

User Driven Attribute Selection Step

Apply RuleStep

Create Mask Step

Attribute RankingStep

SP2Learn Workflow

Registration

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

Before “Convert” After “Convert”

Create Mask

A

B

C

Mask Operations

Mask Parameters

Visualization Panel

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Mask Creation Options in SP2Learn

Ranking of Multiple R/D Rate Maps

Imaginations unbound

Attribute Selection

• Output: Predicted Variable

• Input: Auxiliary Variables

• Check-boxes• Show Table• Prune Tree

Decision Tree Based Modeling

• Tree structure can be represented as a set of rules

Discharge

yes

noyes

no

Recharge DischargeCase A..

Case E.. Case J..

Distance from river ≤ 100 ft?

Soil Type is {sand}?

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

Export Rules

• XML format

Export Rules Option

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Apply Rules

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

On-Line Help

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Other Similar Desktop Solutions

National Center for Supercomputing Applications

GeoLearn

Praveen KumarAssoc. Prof.Civil & Env. Eng.

Peter BajcsyRes. Scientist, NCSA

David TchengNCSA

Wei-Wen FengGrad Student Comp. Sci.

Pratyush SinhaGrad Student Civil and Env. Eng.

Amanda WhiteGrad Student Civil & Env. Eng.

Vikas MehraGrad Student Civil & Env. Eng. David Clutter

NCSA

Ricky RobertsonPost Doc Civil Eng.

Rob KooperNCSA

Ben RuddellGrad Student Civil & Env. Eng.

Qi Li, Grad Student, Dept. of Geology

Chulyun Kim, Grad Student, Comp. Sci.

Yakov Keselman, NCSA

Tim Nee, Undergraduate Student, NCSA

GeoLearn Functionality Overview

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RAW DATA

INFORMATION

KNOWLEDGE

Types of Research Using Geolearn

• (1) Sensitivity Studies• Data integration and masking variables• Data modeling variables

• (2) Modeling Studies: • Find Common Characteristics

• E.g., using clustering• Find Deviations

• Data-driven modeling• Prediction analysis

• (3) Variable Relevance Ranking52

Deviations ~ Regression Tree Based Modeling of NDVI

corn &soybeans

irrigated river valleymany crops

May 2004, Residual=|RT predicted – measured|

Example of Variable Relevance Ranking

• Prediction of Chl-A in May 2002 using Support Vector Machine (SVM) data-driven modeling technique with linear kernel

• Example conclusion: Chl-A is likely to depend on temperature and unlikely to depend on elevation

Adding VariablesTemperature Slope & Elevation

Ground Measurements

Remote Sensing Measurements

Spatially sparse and temporally incomplete ground measurements & spatially dense and temporally sparse remote sensing measurements of temperature

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Summary and Open Problems

Summary

• Sp2Learn, Geolearn and other solutions are focusing primarily on supporting analytical flow

• They are java-based desktop solutions• They address software and data integration,

as well as machine learning and exploratory analyses

Imaginations unbound

Open Problems• Automation

• Flexible creation of scripts/workflows • Provenance gathering

• Data sharing and collaboration• Data sharing is supported by exporting intermediate and

resulting files• Parameter sharing?• Visualization sharing?

• Computational and visualization scalability• Out-of-core representation• Utilization of distributed or high-performance computational

resources?

Imaginations unbound

Example: Common Characteristics ~ Eco-region level II

Unsupervised K-means Clustering of Large Size Data ~ (6 Million Pixels) x (29 Variables) x (8 bytes) = 1.4GB

Example: Processing and Visualization of Large Size Data

• Out-of-core representation

• Total file size:372MB

• Total memoryfootprint:less than 30MB

Scalability of algorithms ? Grid Computing ? Algorithms behind web services ?

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Software and Test Data Download

• Sp2Learn and Geolearn• Download web page of Image Spatial

Data Analysis group at NCSA: http://isda.ncsa.uiuc.edu/download/

Thank you!

• Questions: • Peter Bajcsy pbajcsy@ncsa.uiuc.edu

• Need More Details• Sp2Learn Project URL:

http://isda.ncsa.uiuc.edu/groundwater/• GeoLearn Project URL:

http://isda.ncsa.uiuc.edu/geolearn/geolearn.html

• Publications: http://isda.ncsa.uiuc.edu

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Backup

Acknowledgement

• This research was partially supported by National Aeronautics and Space Administration (NASA), the Faculty Fellow Program at National Center for Supercomputing Applications (NCSA), the Illinois State Water Survey (ISWS) and by NCSA Industrial partners

• The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the sponsors.

• Contributions by the members of the Image Spatial Data Analysis (ISDA) Group at NCSA and our collaborators from ISWS, CEE UIUC and International Institutions

Imaginations unbound

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Common Characteristics ~ Eco-region level III

Model Interpretation by Input Variable Relevance Assignment

Pixel TN EVI

1 3.38 0.22

2 5.1 0.3

Extracted Table

Multiple Learning Methods

SVMKNNRT

Relevance Analyzer

VisualizationModel Relevance

Sample Relevance

Prediction Result

Prediction Error

Point and Raster Integration

Pixel TN

1 3.38

2 5.1

Support of Analytical Flows

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Automation Support

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Sharing and Collaboration Support