Summary: This dataset contains high-resolution aerial imagery data used to classify urban land cover into 9 types, such as trees, grass, and buildings.
| Parameter | Value |
|---|---|
| Name | Urban Land Cover |
| Labeled | Yes |
| Time Series | No |
| Simulation | No |
| Missing Values | No |
| Dataset Characteristics | Multivariate |
| Feature Type | Real |
| Associated Tasks | Classification |
| Number of Instances | 168 |
| Number of Features | 148 |
| Date Donated | 2014-03-26 |
| Source | UCI Machine Learning Repository |
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.
Urban land cover, Aerial imagery, Environmental monitoring, Remote sensing, Land use classification