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d4ec9f0 533e190 d4ec9f0 533e190 d4ec9f0 533e190 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""UC Merced Land Use Dataset"""
import os
import datasets
_CITATION = """\
@inproceedings{yang2010bagofvisualwords,
author = {Yi Yang and Shawn Newsam},
title = {Bag-Of-Visual-Words and Spatial Extensions for Land-Use Classification},
booktitle = {ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS)},
year = {2010}
}
"""
_DESCRIPTION = """\
This is a 21 class land use image dataset meant for research purposes.
There are 100 images for each of the following classes:
- agricultural
- airplane
- baseballdiamond
- beach
- buildings
- chaparral
- denseresidential
- forest
- freeway
- golfcourse
- harbor
- intersection
- mediumresidential
- mobilehomepark
- overpass
- parkinglot
- river
- runway
- sparseresidential
- storagetanks
- tenniscourt
Each image measures 256x256 pixels.
The images were manually extracted from large images from the
USGS National Map Urban Area Imagery collection for various urban areas around
the country. The pixel resolution of this public domain imagery is 1 foot.
For more information about the original UC Merced Land Use dataset,
please visit the official dataset page:
http://weegee.vision.ucmerced.edu/datasets/landuse.html
Please refer to the original dataset source for any additional details,
citations, or specific usage guidelines provided by the dataset creators.
"""
_HOMEPAGE = "http://weegee.vision.ucmerced.edu/datasets/landuse.html"
_LICENSE = "cc0-1.0"
_DATA_URL = "http://weegee.vision.ucmerced.edu/datasets/UCMerced_LandUse.zip"
_LABEL_NAMES = [
"agricultural",
"airplane",
"baseballdiamond",
"beach",
"buildings",
"chaparral",
"denseresidential",
"forest",
"freeway",
"golfcourse",
"harbor",
"intersection",
"mediumresidential",
"mobilehomepark",
"overpass",
"parkinglot",
"river",
"runway",
"sparseresidential",
"storagetanks",
"tenniscourt",
]
class UCMercedLandUse(datasets.GeneratorBasedBuilder):
"""A 21 class land use image dataset."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="ucmerced_landuse",
version=VERSION,
description="UC Merced Land Use Dataset",
),
]
DEFAULT_CONFIG_NAME = "ucmerced_landuse"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"img": datasets.Image(),
"label": datasets.features.ClassLabel(names=_LABEL_NAMES),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract("UCMerced_LandUse.zip")
class_dirs = [
os.path.join(data_dir, "UCMerced_LandUse/Images", label)
for label in _LABEL_NAMES
]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"class_dirs": class_dirs,
"split": "train",
},
),
]
def _generate_examples(self, class_dirs, split):
key = 0
for class_dir in class_dirs:
class_label = os.path.basename(class_dir)
# Iterate through the images in the class directory
for image_filename in os.listdir(class_dir):
image_path = os.path.join(class_dir, image_filename)
yield key, {
"img": image_path,
"label": class_label,
}
key += 1
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