Wilt
Donated on 3/12/2014
High-resolution Remote Sensing data set (Quickbird). Small number of training samples of diseased trees, large number for other land cover. Testing data set from stratified random sample of image.
Dataset Characteristics
Multivariate
Subject Area
Biology
Associated Tasks
Classification
Feature Type
-
# Instances
4889
# Features
-
Dataset Information
Additional Information
This data set contains some training and testing data from a remote sensing study by Johnson et al. (2013) that involved detecting diseased trees in Quickbird imagery. There are few training samples for the 'diseased trees' class (74) and many for 'other land cover' class (4265). The data set consists of image segments, generated by segmenting the pansharpened image. The segments contain spectral information from the Quickbird multispectral image bands and texture information from the panchromatic (Pan) image band. The testing data set is for the row with “Segmentation scale 15†segments and “original multi-spectral image†Spectral information in Table 2 of the reference (i.e. row 5). Please see the reference below for more information on the data set, and please cite the reference if you use this data set. Enjoy! Files training.csv: training data set (4339 image segments) testing.csv: testing data set (500 image segments)
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no | |||||
| no |
0 to 6 of 6
Additional Variable Information
class: 'w' (diseased trees), 'n' (all other land cover) GLCM_Pan: GLCM mean texture (Pan band) Mean_G: Mean green value Mean_R: Mean red value Mean_NIR: Mean NIR value SD_Pan: Standard deviation (Pan band)
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset wilt = fetch_ucirepo(id=285) # data (as pandas dataframes) X = wilt.data.features y = wilt.data.targets # metadata print(wilt.metadata) # variable information print(wilt.variables)
Johnson,Brian. (2014). Wilt. UCI Machine Learning Repository. https://doi.org/10.24432/C5KS4M.
@misc{misc_wilt_285,
author = {Johnson,Brian},
title = {{Wilt}},
year = {2014},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5KS4M}
}Creators
Brian Johnson
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.