GeoLearn has been prototyped as a novel simulation and exploratory environment for prediction modeling from remote
sensing imagery, and large size geospatial raster and vector data. The GeoLearn framework has the functionality to read
data sets from local and remote sites; extract features like slope from elevation; mosaic tiles; perform quality
assurance of remotely sensed images; integrate images; spatially
select pixels by masking with boundaries, geo-points, maps with categorical variables, thresholded maps with continuous
variables or painted regions using primitives; extract pixels over a mask, perform data-driven modeling using
machine learning techniques, provide interpretation
of models in terms of variable relevance and visualize a variety of input, output and intermediate data.
We illustrate the application of the framework to exploring vegetation greenness as a function of climate, terrain,
water and soil. The regions with large deviations from predicted values are shown in the image
(left).
Overlay of the elevation map of Illinois and the Algae predicted values at water station locations.
In our project visualization and data mining tools are applied to Algal biomass prediction in Illinois streams.
The problem of algal biomass prediction in Illinois streams lies in explaining the variability in
algal biomass measured as chlorophyl a, based on nutrients (total or dissolved nitrogen, and total or dissolved
phosphorus) and other variables (water velocity, canopy cover along the streambank, stream width/depth, etc.).
Algae are either the direct or indirect cause of most problems related to nutrient enrichment.
This project was supported by the National Science Foundation and National Center for Supercomputing
Applications.
Results of pattern analysis (cell-by-cell recharge estimation) from the
recharge/discharge and soil drainage maps of the Buena Vista Basin, Wisconsin.
We focused on the problem of modeling groundwater recharge and discharge rates.
The phenomena related to groundwater recharge and discharge result from a set of complex,
uncertain processes and are generally difficult to study.
We provide test data to illustrate how to incorporate and mine slope, soil type and proximity
to water bodies for predicting groundwater recharge and discharge (R/D) rate models.
The joint research of NCSA and Illinois State Water Survey (ISWS)
combines the computer science and ground water science expertise,
and leveraged numerical methods and image processing algorithms to efficiently estimate R/D rates.
The results of our joint research help hydro-geologists to better understand zonation delineation.
The work in progress is being tested against an intensively studied field site in Wisconsin and it
will be applied immediately to several groundwater studies in northeastern Illinois.
Spatial Pattern to Learn (Sp2Learn) software presents a framework for
accurate estimation of geospatial models from sparse field measurements using image processing and machine learning.
Confluence of the San Juan and San Carlos rivers; surface image with the near-IR map overlayed.
The project of visualization and analysis of the aerial land images of Costa Rica, obtained from the CARTA 2003
and CARTA 2005 missions is part of a broader
initiative called the Advanced Research and Technology Collaboratory for the Americas (ARTCA). Activities of
researchers from the Instituto Tecnologico de Costa Rica (ITCR), Centro Nacional de Alta Tecnología (CeNAT), National Center
for Supercomputing Applications (NCSA) and universities are coordinated by CeNAT in Costa Rica.
Project Overview: (a) Pre-process and integrate large size
airborne imagery from three sources for
two distinct years 2003 and 2005. (b) Manage the data and enable easy web access for browsing.
(c) Prepare a methodology, data workflows and
optimal parameters for next CARTA mission.
