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
Climate and Environment
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).
Dataset Files
| File | Size |
|---|---|
| testing.csv | 402.4 KB |
| training.csv | 133.8 KB |
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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)
Johnson, B. (2013). Urban Land Cover [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C53S48.
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.