# World Heightmaps 360 V1
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.
task_categories:unconditional-image-generation task_categories:image-classification task_categories:text-to-image size_categories:100K<n<1M license:apache-2.0 region:us
{"license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["unconditional-image-generation", "image-classification", "text-to-image"], "pretty_name": "World Heightmaps 360 V1"}
TAGS
#task_categories-unconditional-image-generation #task_categories-image-classification #task_categories-text-to-image #size_categories-100K<n<1M #license-apache-2.0 #region-us 

# World Heightmaps 360 V1
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.


2. Split PNGs into 100 patches with Split Image.


3. Hand pick a dataset of corrupted and uncorrupted heightmaps then train a discriminator to automatically filter the whole dataset.
# World Heightmaps 360 V1
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.


2. Split PNGs into 100 patches with Split Image.


3. Hand pick a dataset of corrupted and uncorrupted heightmaps then train a discriminator to automatically filter the whole dataset.
TAGS
#task_categories-unconditional-image-generation #task_categories-image-classification #task_categories-text-to-image #size_categories-100K<n<1M #license-apache-2.0 #region-us 
 # World Heightmaps 360 V1
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.


2. Split PNGs into 100 patches with Split Image.


3. Hand pick a dataset of corrupted and uncorrupted heightmaps then train a discriminator to automatically filter the whole dataset.
passage: TAGS
#task_categories-unconditional-image-generation #task_categories-image-classification #task_categories-text-to-image #size_categories-100K<n<1M #license-apache-2.0 #region-us 
# World Heightmaps 360 V1
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.


2. Split PNGs into 100 patches with Split Image.


3. Hand pick a dataset of corrupted and uncorrupted heightmaps then train a discriminator to automatically filter the whole dataset.