Create README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- unconditional-image-generation
+- image-classification
+- text-to-image
+pretty_name: World Heightmaps 360 V1
+size_categories:
+- 100K<n<1M
+---
+# World Heightmaps 360px
+This is a dataset of 360x360 Earth heightmaps generated from [SRTM 1 Arc-Second Global](https://huggingface.co/datasets/hayden-donnelly/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.
+```python
+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')
+```
+
+2. Split PNGs into 100 patches with Split Image.
+```python
+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)
+```
+
+3. Hand pick a dataset of corrupted and uncorrupted heightmaps then train a discriminator to automatically filter the whole dataset.
+
+4. Compile images into parquet files.
+```python
+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)
+```