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


Subject Area

Climate and Environment

Associated Tasks


Feature Type


# Instances


# Features


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?


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).


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Brian Johnson


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