Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact

Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Covertype Data Set
Download: Data Folder, Data Set Description

Abstract: Forest CoverType dataset

Data Set Characteristics:  


Number of Instances:




Attribute Characteristics:

Categorical, Integer

Number of Attributes:


Date Donated


Associated Tasks:


Missing Values?


Number of Web Hits:



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 '@' or Dr. Denis J. Dean, denis.dean '@'

Donors of database:

1. Jock A. Blackard (jblackard '@'
GIS Coordinator
USFS - Forest Inventory & Analysis
Rocky Mountain Research Station
507 25th Street
Ogden, UT 84401

2. Dr. Denis J. Dean (denis.dean '@'
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 '@'
Associate Professor
Department of Computer Science
Colorado State University
Fort Collins, CO 80523 USA

Data Set Information:

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.

Attribute Information:

Given is the attribute name, attribute type, the measurement unit and a brief description. The forest cover type is the classification problem. The order of this listing corresponds to the order of numerals along the rows of the database.

Name / Data Type / Measurement / Description

Elevation / quantitative /meters / Elevation in meters
Aspect / quantitative / azimuth / Aspect in degrees azimuth
Slope / quantitative / degrees / Slope in degrees
Horizontal_Distance_To_Hydrology / quantitative / meters / Horz Dist to nearest surface water features
Vertical_Distance_To_Hydrology / quantitative / meters / Vert Dist to nearest surface water features
Horizontal_Distance_To_Roadways / quantitative / meters / Horz Dist to nearest roadway
Hillshade_9am / quantitative / 0 to 255 index / Hillshade index at 9am, summer solstice
Hillshade_Noon / quantitative / 0 to 255 index / Hillshade index at noon, summer soltice
Hillshade_3pm / quantitative / 0 to 255 index / Hillshade index at 3pm, summer solstice
Horizontal_Distance_To_Fire_Points / quantitative / meters / Horz Dist to nearest wildfire ignition points
Wilderness_Area (4 binary columns) / qualitative / 0 (absence) or 1 (presence) / Wilderness area designation
Soil_Type (40 binary columns) / qualitative / 0 (absence) or 1 (presence) / Soil Type designation
Cover_Type (7 types) / integer / 1 to 7 / Forest Cover Type designation

Relevant Papers:

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.
[Web Link]

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.

Papers That Cite This Data Set1:

Joao Gama and Ricardo Rocha and Pedro Medas. Accurate decision trees for mining high-speed data streams. KDD. 2003. [View Context].

Nikunj C. Oza and Stuart J. Russell. Experimental comparisons of online and batch versions of bagging and boosting. KDD. 2001. [View Context].

Zoran Obradovic and Slobodan Vucetic. Challenges in Scientific Data Mining: Heterogeneous, Biased, and Large Samples. Center for Information Science and Technology Temple University. [View Context].

Arto Klami and Samuel Kaski and Ty n ohjaaja and Janne Sinkkonen. HELSINKI UNIVERSITY OF TECHNOLOGY Department of Engineering Physics and Mathematics Arto Klami Regularized Discriminative Clustering. Regularized Discriminative Clustering. [View Context].

Chris Giannella and Bassem Sayrafi. An Information Theoretic Histogram for Single Dimensional Selectivity Estimation. Department of Computer Science, Indiana University Bloomington. [View Context].

Johannes Furnkranz. Round Robin Rule Learning. Austrian Research Institute for Artificial Intelligence. [View Context].

Citation Request:

Reuse of this database is unlimited with retention of copyright notice for Jock A. Blackard and Colorado State University.

[1] Papers were automatically harvested and associated with this data set, in collaboration with

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML