Datasets:
File size: 3,203 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': agricultural
'1': airplane
'2': baseball_diamond
'3': beach
'4': buildings
'5': chaparral
'6': dense_residential
'7': forest
'8': freeway
'9': golf_course
'10': harbor
'11': intersection
'12': medium_residential
'13': mobile_home_park
'14': overpass
'15': parking_lot
'16': river
'17': runway
'18': sparse_residential
'19': storage_tanks
'20': tennis_court
splits:
- name: train
num_bytes: 252492192
num_examples: 2100
download_size: 252492968
dataset_size: 252492192
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
---
# Ucm Land Use Classification
UC Merced Land Use (UCM) scene classification: 2,100 aerial RGB image tiles of 256 x 256 pixels at about 0.3 m (1 foot) resolution, 100 per class across 21 land-use classes (agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense/medium/sparse residential, forest, freeway, golf course, harbor, intersection, mobile home park, overpass, parking lot, river, runway, storage tanks, tennis court). The tiles were cut by Yang and Newsam (2010) from USGS National Map Urban Area Imagery of US towns; this copy is the CC BY 4.0 Figshare re-host used by the Remote Sensing study on federated fine-tuning (Fed-RSAdapter). A handful of tiles are a few pixels smaller than 256 x 256, as in the original release. The dataset contains 2,100 images across 21 classes: agricultural, airplane, baseball_diamond, beach, buildings, chaparral, dense_residential, forest, freeway, golf_course, harbor, intersection, medium_residential, mobile_home_park, overpass, parking_lot, river, runway, sparse_residential, storage_tanks, tennis_court.
Images per class:
- agricultural: 100
- airplane: 100
- baseball_diamond: 100
- beach: 100
- buildings: 100
- chaparral: 100
- dense_residential: 100
- forest: 100
- freeway: 100
- golf_course: 100
- harbor: 100
- intersection: 100
- medium_residential: 100
- mobile_home_park: 100
- overpass: 100
- parking_lot: 100
- river: 100
- runway: 100
- sparse_residential: 100
- storage_tanks: 100
- tennis_court: 100
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@inproceedings{yang2010bag,
title={Bag-of-visual-words and spatial extensions for land-use classification},
author={Yang, Yi and Newsam, Shawn},
booktitle={Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems},
pages={270--279},
year={2010},
doi={10.1145/1869790.1869829}
}
```
The dataset itself can be cited as:
Nouman Ali, & Zafar, B. (2018). *UCM image dataset* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.6085976.V2
*This dataset was reformatted from its original format to match HuggingFace standards.*
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