|
--- |
|
dataset_info: |
|
features: |
|
- name: image |
|
dtype: image |
|
- name: label_1 |
|
dtype: |
|
class_label: |
|
names: |
|
'0': unutilized land |
|
'1': commercial land |
|
'2': public service land |
|
'3': transportation land |
|
'4': industrial land |
|
'5': water area |
|
'6': residential land |
|
'7': agriculture land |
|
- name: label_2 |
|
dtype: |
|
class_label: |
|
names: |
|
'0': dam |
|
'1': religious land |
|
'2': rock land |
|
'3': sparse shrub land |
|
'4': arable land |
|
'5': factory area |
|
'6': detached house |
|
'7': desert |
|
'8': lake |
|
'9': power station |
|
'10': beach |
|
'11': ice land |
|
'12': bare land |
|
'13': island |
|
'14': woodland |
|
'15': mobile home park |
|
'16': railway area |
|
'17': river |
|
'18': grassland |
|
'19': apartment |
|
'20': special land |
|
'21': port area |
|
'22': commercial area |
|
'23': highway area |
|
'24': mining area |
|
'25': sports land |
|
'26': airport area |
|
'27': leisure land |
|
- name: label_3 |
|
dtype: |
|
class_label: |
|
names: |
|
'0': dam |
|
'1': parking lot |
|
'2': greenhouse |
|
'3': pier |
|
'4': bridge |
|
'5': mine |
|
'6': rock land |
|
'7': baseball field |
|
'8': apron |
|
'9': tennis court |
|
'10': sparse shrub land |
|
'11': works |
|
'12': oil field |
|
'13': meadow |
|
'14': ground track field |
|
'15': detached house |
|
'16': golf course |
|
'17': forest |
|
'18': desert |
|
'19': lake |
|
'20': beach |
|
'21': paddy field |
|
'22': ice land |
|
'23': bare land |
|
'24': storage tank |
|
'25': basketball court |
|
'26': island |
|
'27': substation |
|
'28': mobile home park |
|
'29': cemetery |
|
'30': quarry |
|
'31': solar power plant |
|
'32': helipad |
|
'33': roundabout |
|
'34': runway |
|
'35': wastewater plant |
|
'36': river |
|
'37': apartment |
|
'38': dry field |
|
'39': intersection |
|
'40': swimming pool |
|
'41': commercial area |
|
'42': church |
|
'43': road |
|
'44': orchard |
|
'45': terraced field |
|
'46': stadium |
|
'47': train station |
|
'48': railway |
|
'49': viaduct |
|
'50': wind turbine |
|
splits: |
|
- name: train |
|
num_bytes: 871962498 |
|
num_examples: 10000 |
|
download_size: 871644115 |
|
dataset_size: 871962498 |
|
license: other |
|
task_categories: |
|
- image-classification |
|
- zero-shot-image-classification |
|
--- |
|
# Dataset Card for "Million-AID" |
|
|
|
## Dataset Description |
|
|
|
- **Paper** [On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf) |
|
- **Split** Train |
|
|
|
## Split Information |
|
|
|
This HuggingFace dataset repository contains just the Train split. |
|
|
|
### Licensing Information |
|
|
|
[CC BY-NC-ND 4.0](https://competitions.codalab.org/competitions/35974#learn_the_details-terms-and-conditions) |
|
|
|
## Citation Information |
|
|
|
[On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf) |
|
|
|
``` |
|
@article{long2021creating, |
|
title = {On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid}, |
|
author = {Long, Yang and Xia, Gui-Song and Li, Shengyang and Yang, Wen and Yang, Michael Ying and Zhu, Xiao Xiang and Zhang, Liangpei and Li, Deren}, |
|
year = 2021, |
|
journal = {IEEE Journal of selected topics in applied earth observations and remote sensing}, |
|
publisher = {IEEE}, |
|
volume = 14, |
|
pages = {4205--4230} |
|
} |
|
``` |
