Urban Land Cover
Donated on 3/26/2014
Classification of urban land cover using high resolution aerial imagery. Intended to assist sustainable urban planning efforts.
Dataset Characteristics
Multivariate
Subject Area
Physical Science
Associated Tasks
Classification
Feature Type
-
# Instances
168
# Features
148
Dataset Information
Additional Information
Contains training and testing data for classifying a high resolution aerial image into 9 types of urban land cover. Multi-scale spectral, size, shape, and texture information are used for classification. There are a low number of training samples for each class (14-30) and a high number of classification variables (148), so it may be an interesting data set for testing feature selection methods. The testing data set is from a random sampling of the image. Class is the target classification variable. The land cover classes are: trees, grass, soil, concrete, asphalt, buildings, cars, pools, shadows.
Has Missing Values?
No
Variable Information
LEGEND Class: Land cover class (nominal) BrdIndx: Border Index (shape variable) Area: Area in m2 (size variable) Round: Roundness (shape variable) Bright: Brightness (spectral variable) Compact: Compactness (shape variable) ShpIndx: Shape Index (shape variable) Mean_G: Green (spectral variable) Mean_R: Red (spectral variable) Mean_NIR: Near Infrared (spectral variable) SD_G: Standard deviation of Green (texture variable) SD_R: Standard deviation of Red (texture variable) SD_NIR: Standard deviation of Near Infrared (texture variable) LW: Length/Width (shape variable) GLCM1: Gray-Level Co-occurrence Matrix [i forget which type of GLCM metric this one is] (texture variable) Rect: Rectangularity (shape variable) GLCM2: Another Gray-Level Co-occurrence Matrix attribute (texture variable) Dens: Density (shape variable) Assym: Assymetry (shape variable) NDVI: Normalized Difference Vegetation Index (spectral variable) BordLngth: Border Length (shape variable) GLCM3: Another Gray-Level Co-occurrence Matrix attribute (texture variable) Note: These variables repeat for each coarser scale (i.e. variable_40, variable_60, ...variable_140).
Johnson,Brian. (2014). Urban Land Cover. UCI Machine Learning Repository. https://doi.org/10.24432/C53S48.
@misc{misc_urban_land_cover_295,
author = {Johnson,Brian},
title = {{Urban Land Cover}},
year = {2014},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C53S48}
}pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset urban_land_cover = fetch_ucirepo(id=295) # data (as pandas dataframes) X = urban_land_cover.data.features y = urban_land_cover.data.targets # metadata print(urban_land_cover.metadata) # variable information print(urban_land_cover.variables)
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.