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ovolve/public-forestcast-demo-colab.ipynb

Created October 31, 2025 14:13
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Public forestcast demo colab
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public-forestcast-demo-colab.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ovolve/9b86c54891ff1c097cd3c78bbbd131b3/public-forestcast-demo-colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"Copyright 2025 Google LLC.\n",
"\n",
"SPDX-License-Identifier: Apache-2.0"
],
"metadata": {
"id": "F5JqLWusf15a"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZQDSP3xMAfQR"
},
"outputs": [],
"source": [
"import datetime\n",
"import tensorflow_datasets as tfds"
]
},
{
"cell_type": "code",
"source": [
"DATA_DIR = \"gs://nature-trace/datasets/tfds\"\n",
"ds_all, info = tfds.load(\n",
" \"forestcast/southeast_asia:1.0.0\",\n",
" data_dir=DATA_DIR,\n",
" download=False,\n",
" try_gcs=True,\n",
" with_info=True,\n",
")\n",
"\n",
"info"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "olHeMGbgAom5",
"outputId": "93baa082-90ca-47d1-8e68-b19ff00dbb00"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tfds.core.DatasetInfo(\n",
" name='forestcast',\n",
" full_name='forestcast/southeast_asia/1.0.0',\n",
" description=\"\"\"\n",
" Forestcast dataset.\n",
" \"\"\",\n",
" homepage='https://www.tensorflow.org/datasets/catalog/forestcast',\n",
" data_dir='gs://nature-trace/datasets/tfds/forestcast/southeast_asia/1.0.0',\n",
" file_format=tfrecord,\n",
" download_size=Unknown size,\n",
" dataset_size=7.52 TiB,\n",
" features=FeaturesDict({\n",
" 'ccdc/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'ccdc/pixels': Tensor(shape=(None, 256, 256, 3), dtype=float32),\n",
" 'ccdc/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'fdap_palm/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'fdap_palm/pixels': Tensor(shape=(None, 256, 256, 1), dtype=float32),\n",
" 'fdap_palm/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'gfw_forest_change/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'gfw_forest_change/pixels': Tensor(shape=(None, 256, 256, 3), dtype=float32),\n",
" 'gfw_forest_change/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'gfw_forest_change_imagery/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'gfw_forest_change_imagery/pixels': Tensor(shape=(None, 256, 256, 4), dtype=float32),\n",
" 'gfw_forest_change_imagery/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'gmd_mangrove/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'gmd_mangrove/pixels': Tensor(shape=(None, 256, 256, 1), dtype=float32),\n",
" 'gmd_mangrove/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'gpw/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'gpw/pixels': Tensor(shape=(None, 256, 256, 1), dtype=float32),\n",
" 'gpw/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'grip/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'grip/pixels': Tensor(shape=(None, 256, 256, 2), dtype=float32),\n",
" 'grip/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'gwl_wetland/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'gwl_wetland/pixels': Tensor(shape=(None, 256, 256, 1), dtype=float32),\n",
" 'gwl_wetland/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'metadata/end_time': string,\n",
" 'metadata/start_time': string,\n",
" 'sentinel_2/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'sentinel_2/pixels': Tensor(shape=(None, 256, 256, 6), dtype=float32),\n",
" 'sentinel_2/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" 'srtm/band_names': Tensor(shape=(None,), dtype=string),\n",
" 'srtm/pixels': Tensor(shape=(None, 256, 256, 1), dtype=float32),\n",
" 'srtm/timestamp_ms': Tensor(shape=(None,), dtype=int64),\n",
" }),\n",
" supervised_keys=None,\n",
" disable_shuffling=False,\n",
" nondeterministic_order=False,\n",
" splits={\n",
" '2017-00': <SplitInfo num_examples=11052, num_shards=3818>,\n",
" '2018-00': <SplitInfo num_examples=10889, num_shards=3790>,\n",
" '2019-00': <SplitInfo num_examples=10829, num_shards=3798>,\n",
" '2020-00': <SplitInfo num_examples=11057, num_shards=3824>,\n",
" '2021-00': <SplitInfo num_examples=10969, num_shards=3791>,\n",
" '2022-00': <SplitInfo num_examples=10970, num_shards=3843>,\n",
" '2023-00': <SplitInfo num_examples=10895, num_shards=3791>,\n",
" },\n",
" citation=\"\"\"\"\"\",\n",
")"
]
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"source": [
"# Get a batch of 4 examples for a selected split.\n",
"split = \"2023-00\"\n",
"batch = next(ds_all[split].batch(4).as_numpy_iterator())\n",
"\n",
"for k, v in batch.items():\n",
" print(k, v.shape, v.dtype)"
],
"metadata": {
"id": "NpSTCZPrAt4u"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def to_datetime(encoded_timestamp: bytes):\n",
" timestamp_ms = int(encoded_timestamp) // 1000\n",
" return datetime.datetime.fromtimestamp(\n",
" timestamp_ms, tz=datetime.timezone.utc)\n",
"\n",
"print(to_datetime(batch[\"metadata/start_time\"][0]))\n",
"print(to_datetime(batch[\"metadata/end_time\"][0]))"
],
"metadata": {
"id": "ohm8TFW5pHYK"
},
"execution_count": null,
"outputs": []
}
]
}
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