heightmap
imagewidth (px)
360
360
latitude
stringclasses
115 values
longitude
stringclasses
315 values
s07
e023
s26
w062
n28
e111
n06
w006
n47
e097
n28
e030
n38
w084
n59
e044
n41
e065
s03
e121
s03
e016
n17
w010
n59
e108
n32
e093
s09
w048
n24
e081
n46
e106
s10
w073
n47
e101
s19
e132
s24
e046
s34
e143
s03
w075
s27
w055
n24
w014
n45
e054
n06
e014
n24
e087
s25
e118
s26
e016
n48
e123
n14
e011
n56
e159
n27
w012
n56
e101
s21
w069
n45
e064
n08
e042
n05
e039
n44
e142
n40
w125
n21
e022
n23
e081
n28
e109
s41
w065
n45
e026
s10
e017
n30
e079
n34
e046
n26
e104
n43
e001
n46
w124
n29
e025
n22
w003
n26
e087
n44
e142
n58
e067
n24
e074
n55
e099
n48
w094
n35
w114
n47
w070
n46
w114
n33
e080
n57
w122
n34
e006
n55
w063
s06
w071
s27
e139
n35
e106
n33
e077
n19
e084
n28
e070
n37
e088
n33
e050
s32
w058
s02
e015
n37
e041
n04
w072
n42
e081
s31
e127
n04
e029
n10
e026
s13
w050
n41
w111
s27
e017
n16
w010
n20
e015
n07
e033
n27
w010
n30
e010
n41
e084
s48
w067
s26
e135
n25
e094
s39
e174
s16
e032
s05
w058
s05
w061
n48
e052

World Heightmaps 360px

This is a dataset of 360x360 Earth heightmaps generated from SRTM 1 Arc-Second Global. Each heightmap is labelled according to its latitude and longitude. There are 573,995 samples.

Method

  1. Convert GeoTIFFs into PNGs with Python and Rasterio.
import rasterio
import matplotlib.pyplot as plt
import os

input_directory = '...'
output_directory = '...'
file_list = os.listdir(input_directory)

for i in range(len(file_list)):
    image = rasterio.open(input_directory + file_list[i])
    plt.imsave(output_directory + file_list[i][0:-4] + '.png', image.read(1), cmap='gray')
  1. Split PNGs into 100 patches with Split Image.
from split_image import split_image
import os

input_directory = '...'
output_directory = '...'
file_list = os.listdir(input_directory)

for i in range(len(file_list)):
    split_image(input_directory + file_list[i], 10, 10, should_square=True, should_cleanup=False, output_dir=output_directory)
  1. Hand pick a dataset of corrupted and uncorrupted heightmaps then train a discriminator to automatically filter the whole dataset.

  2. Compile images into parquet files.

import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
from PIL import Image
import os
import io
import json

samples_per_file = 6_000

root_dir = 'data/datasets/world-heightmaps-360px-png'
df = pd.read_csv(os.path.join(root_dir, 'metadata.csv'))
df = df.sample(frac=1).reset_index(drop=True)

def save_table(image_data, table_number):
    print(f'Entries in table {table_number}: {len(image_data)}')
    schema = pa.schema(
        fields=[
            ('heightmap', pa.struct([('bytes', pa.binary()), ('path', pa.string())])),
            ('latitude', pa.string()),
            ('longitude', pa.string())
        ],
        metadata={
            b'huggingface': json.dumps({
                'info': {
                    'features': {
                        'heightmap': {'_type': 'Image'},
                        'latitude': {'_type': 'Value', 'dtype': 'string'},
                        'longitude': {'_type': 'Value', 'dtype': 'string'}
                    }
                }
            }).encode('utf-8')
        }
    )

    table = pa.Table.from_pylist(image_data, schema=schema)
    pq.write_table(table, f'data/world-heightmaps-360px-parquet/{str(table_number).zfill(4)}.parquet')

image_data = []
samples_in_current_file = 0
current_file_number = 0
for i, row in df.iterrows():
    if samples_in_current_file >= samples_per_file:
        save_table(image_data, current_file_number)
        image_data = []
        samples_in_current_file = 0
        current_file_number += 1
    samples_in_current_file += 1
    image_path = row['file_name']
    with Image.open(os.path.join(root_dir, image_path)) as image:
        image_bytes = io.BytesIO()
        image.save(image_bytes, format='PNG')
        image_dict = {
            'heightmap': {
                'bytes': image_bytes.getvalue(),
                'path': image_path
            },
            'latitude': str(row['latitude']),
            'longitude': str(row['longitude'])
        }
        image_data.append(image_dict)

save_table(image_data, current_file_number)
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