Classify Cover Types
Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types).
This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices.
Some background information for these four wilderness areas: Neota (area 2) probably has the highest mean elevational value of the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3) would have a lower mean elevational value, while Cache la Poudre (area 4) would have the lowest mean elevational value.
As for primary major tree species in these areas, Neota would have spruce/fir (type 1), while Rawah and Comanche Peak would probably have lodgepole pine (type 2) as their primary species, followed by spruce/fir and aspen (type 5). Cache la Poudre would tend to have Ponderosa pine (type 3), Douglas-fir (type 6), and cottonwood/willow (type 4).
The Rawah and Comanche Peak areas would tend to be more typical of the overall dataset than either the Neota or Cache la Poudre, due to their assortment of tree species and range of predictive variable values (elevation, etc.) Cache la Poudre would probably be more unique than the others, due to its relatively low elevation range and species composition.
Blackard, Jock A. and Denis J. Dean. 2000. "Comparative Accuracies of Artificial Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables." Computers and Electronics in Agriculture 24(3):131-151.
Blackard, Jock A. and Denis J. Dean. 1998. "Comparative Accuracies of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types from Cartographic Variables." Second Southern Forestry GIS Conference. University of Georgia. Athens, GA. Pages 189-199.
Blackard, Jock A. 1998. "Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types." Ph.D. dissertation. Department of Forest Sciences. Colorado State University. Fort Collins, Colorado. 165 pages.
Original Owners of Database:
Remote Sensing and GIS Program
Department of Forest Sciences
College of Natural Resources
Colorado State University
Fort Collins, CO 80523
(contact Jock A. Blackard, jblackard '@' fs.fed.us or Dr. Denis J. Dean, denis.dean '@' utdallas.edu)
Donors of database:
1. Jock A. Blackard (jblackard '@' fs.fed.us)
USFS - Forest Inventory & Analysis
Rocky Mountain Research Station
507 25th Street
Ogden, UT 84401
2. Dr. Denis J. Dean (denis.dean '@' utdallas.edu)
Program in Geography and Geospatial Sciences
School of Economic, Political and Policy Sciences
800 West Campbell Rd
Richardson, TX 75080-3021
3. Dr. Charles W. Anderson (anderson '@' cs.colostate.edu)
Department of Computer Science
Colorado State University
Fort Collins, CO 80523 USA
The evaluation of this dataset is done using Area Under the ROC curve (AUC).
Interpreting the AUROC
Computing the AUROC
Source : http://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it
- Use of external data is not permitted. This includes use of pre-trained models.
- Hand-labeling is allowed on the training dataset only. Hand-labeling is not permitted on test data and will be grounds for disqualification.
|1||developer.shrestha||1.00000000||24||April 26, 2018, 6:44 a.m.|
|2||super||0.93565623||4||May 3, 2018, 2:50 a.m.|
|3||uday||0.50057969||1||June 19, 2018, 4:30 a.m.|
Please refer to : https://archive.ics.uci.edu/ml/citation_policy.html
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.