Added sample for geospatial classification

people-and-planet-ai/README.md

@@ -40,3 +40,20 @@ location data.
 [Dataflow]: https://cloud.google.com/dataflow
 [Keras]: https://keras.io
 [Vertex AI]: https://cloud.google.com/vertex-ai
+
+## 🏭 [Coal Plant Predictions -- _geospatial-classification_](geospatial-classification)
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/geospatial-classification/README.ipynb)
+
+This model uses satellite data to predict if a coal plant is turned on and producing carbon emissions. The satellite data comes from [Google Earth Engine.](https://earthengine.google.com/)
+
+* **Model**: 1D Fully Convolutional Network in [TensorFlow]
+* **Creating datasets**: [Sentinel-2] satellite data from [Earth Engine]
+* **Training the model**: [TensorFlow] in [Vertex AI]
+* **Getting predictions**: [TensorFlow] in [Cloud Run]
+
+[Cloud Run]: https://cloud.google.com/run
+[Sentinel-2]: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2
+[Earth Engine]: https://earthengine.google.com/
+[TensorFlow]: https://www.tensorflow.org/
+[Vertex AI]: https://cloud.google.com/vertex-ai

people-and-planet-ai/geospatial-classification/README.md

@@ -0,0 +1,16 @@
+# 🏭 Coal Plant Predictions -- _geospatial-classification_
+
+[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GoogleCloudPlatform/python-docs-samples/blob/main/people-and-planet-ai/geospatial-classification/README.ipynb)
+
+This model uses satellite data to predict if a coal plant is turned on and producing carbon emissions. The satellite data comes from [Google Earth Engine.](https://earthengine.google.com/)
+
+* **Model**: 1D Fully Convolutional Network in [TensorFlow]
+* **Creating datasets**: [Sentinel-2] satellite data from [Earth Engine]
+* **Training the model**: [TensorFlow] in [Vertex AI]
+* **Getting predictions**: [TensorFlow] in [Cloud Run]
+
+[Cloud Run]: https://cloud.google.com/run
+[Sentinel-2]: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2
+[Earth Engine]: https://earthengine.google.com/
+[TensorFlow]: https://www.tensorflow.org/
+[Vertex AI]: https://cloud.google.com/vertex-ai

people-and-planet-ai/geospatial-classification/constraints.txt

@@ -0,0 +1,2 @@
+google-auth==2.5.0
+google-cloud-bigquery==2.32.0

people-and-planet-ai/geospatial-classification/e2e_test.py

@@ -0,0 +1,356 @@
+# Copyright 2022 Google LLC
+#
+# 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.
+
+from datetime import datetime, timedelta
+import logging
+import os
+import platform
+import subprocess
+import time
+from typing import NamedTuple
+import uuid
+
+import ee
+import google.auth
+from google.cloud import aiplatform
+from google.cloud import storage
+import pandas as pd
+import pytest
+import requests
+
+
+PYTHON_VERSION = "".join(platform.python_version_tuple()[0:2])
+
+NAME = f"ppai/geospatial-classification-py{PYTHON_VERSION}"
+
+UUID = uuid.uuid4().hex[0:6]
+PROJECT = os.environ["GOOGLE_CLOUD_PROJECT"]
+REGION = "us-central1"
+
+TIMEOUT_SEC = 30 * 60 # 30 minutes in seconds
+POLL_INTERVAL_SEC = 60 # 1 minute in seconds
+
+VERTEX_AI_SUCCESS_STATE = "PIPELINE_STATE_SUCCEEDED"
+VERTEX_AI_FINISHED_STATE = {
+ "PIPELINE_STATE_SUCCEEDED",
+ "PIPELINE_STATE_FAILED",
+ "PIPELINE_STATE_CANCELLED",
+}
+
+EARTH_ENGINE_SUCCESS_STATE = "SUCCEEDED"
+EARTH_ENGINE_FINISHED_STATE = {"SUCCEEDED"}
+
+BANDS = [
+ "B1",
+ "B2",
+ "B3",
+ "B4",
+ "B5",
+ "B6",
+ "B7",
+ "B8",
+ "B8A",
+ "B9",
+ "B10",
+ "B11",
+ "B12",
+]
+LABEL = "is_powered_on"
+
+IMAGE_COLLECTION = "COPERNICUS/S2"
+SCALE = 10
+
+TRAIN_VALIDATION_SPLIT = 0.7
+
+PATCH_SIZE = 16
+
+credentials, _ = google.auth.default(
+ scopes=["https://www.googleapis.com/auth/cloud-platform"]
+)
+ee.Initialize(credentials, project=PROJECT)
+
+logging.getLogger().setLevel(logging.INFO)
+
+
+@pytest.fixture(scope="session")
+def bucket_name() -> str:
+ storage_client = storage.Client()
+
+ bucket_name = f"{NAME.replace('/', '-')}-{UUID}"
+ bucket = storage_client.create_bucket(bucket_name, location=REGION)
+
+ logging.info(f"bucket_name: {bucket_name}")
+ yield bucket_name
+
+ bucket.delete(force=True)
+
+
+@pytest.fixture(scope="session")
+def test_data(bucket_name: str) -> str:
+ labels_dataframe = pd.read_csv("labeled_geospatial_data.csv")
+ train_dataframe = labels_dataframe.sample(
+ frac=TRAIN_VALIDATION_SPLIT, random_state=200
+ ) # random state is a seed value
+ validation_dataframe = labels_dataframe.drop(train_dataframe.index).sample(frac=1.0)
+
+ train_features = [labeled_feature(row) for row in train_dataframe.itertuples()]
+
+ validation_features = [
+ labeled_feature(row) for row in validation_dataframe.itertuples()
+ ]
+
+ training_task = ee.batch.Export.table.toCloudStorage(
+ collection=ee.FeatureCollection(train_features),
+ description="Training image export",
+ bucket=bucket_name,
+ fileNamePrefix="geospatial_training",
+ selectors=BANDS + [LABEL],
+ fileFormat="TFRecord",
+ )
+
+ training_task.start()
+
+ validation_task = ee.batch.Export.table.toCloudStorage(
+ collection=ee.FeatureCollection(validation_features),
+ description="Validation image export",
+ bucket=bucket_name,
+ fileNamePrefix="geospatial_validation",
+ selectors=BANDS + [LABEL],
+ fileFormat="TFRecord",
+ )
+
+ validation_task.start()
+
+ train_status = None
+ val_status = None
+
+ logging.info("Waiting for data export to complete.")
+ for _ in range(0, TIMEOUT_SEC, POLL_INTERVAL_SEC):
+ train_status = ee.data.getOperation(training_task.name)["metadata"]["state"]
+ val_status = ee.data.getOperation(validation_task.name)["metadata"]["state"]
+ if (
+ train_status in EARTH_ENGINE_FINISHED_STATE
+ and val_status in EARTH_ENGINE_FINISHED_STATE
+ ):
+ break
+ time.sleep(POLL_INTERVAL_SEC)
+
+ assert train_status == EARTH_ENGINE_SUCCESS_STATE
+ assert val_status == EARTH_ENGINE_SUCCESS_STATE
+ logging.info(f"Export finished with status {train_status}")
+
+ yield training_task.name
+
+
+def labeled_feature(row: NamedTuple) -> ee.FeatureCollection:
+ start = datetime.fromisoformat(row.timestamp)
+ end = start + timedelta(days=1)
+ image = (
+ ee.ImageCollection(IMAGE_COLLECTION)
+ .filterDate(start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d"))
+ .select(BANDS)
+ .mosaic()
+ )
+ point = ee.Feature(
+ ee.Geometry.Point([row.lon, row.lat]),
+ {LABEL: row.is_powered_on},
+ )
+ return (
+ image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE))
+ .sampleRegions(ee.FeatureCollection([point]), scale=SCALE)
+ .first()
+ )
+
+
+@pytest.fixture(scope="session")
+def container_image(bucket_name: str) -> str:
+ # https://cloud.google.com/sdk/gcloud/reference/builds/submit
+ container_image = f"gcr.io/{PROJECT}/{NAME}:{UUID}"
+ subprocess.check_call(
+ [
+ "gcloud",
+ "builds",
+ "submit",
+ "serving_app",
+ f"--tag={container_image}",
+ f"--project={PROJECT}",
+ "--machine-type=e2-highcpu-8",
+ "--timeout=15m",
+ "--quiet",
+ ]
+ )
+
+ logging.info(f"container_image: {container_image}")
+ yield container_image
+
+ # https://cloud.google.com/sdk/gcloud/reference/container/images/delete
+ subprocess.check_call(
+ [
+ "gcloud",
+ "container",
+ "images",
+ "delete",
+ container_image,
+ f"--project={PROJECT}",
+ "--force-delete-tags",
+ "--quiet",
+ ]
+ )
+
+
+@pytest.fixture(scope="session")
+def service_url(bucket_name: str, container_image: str) -> str:
+ # https://cloud.google.com/sdk/gcloud/reference/run/deploy
+ service_name = f"{NAME.replace('/', '-')}-{UUID}"
+ subprocess.check_call(
+ [
+ "gcloud",
+ "run",
+ "deploy",
+ service_name,
+ f"--image={container_image}",
+ "--command=gunicorn",
+ "--args=--threads=8,--timeout=0,main:app",
+ "--platform=managed",
+ f"--project={PROJECT}",
+ f"--region={REGION}",
+ "--memory=1G",
+ "--no-allow-unauthenticated",
+ ]
+ )
+
+ # https://cloud.google.com/sdk/gcloud/reference/run/services/describe
+ service_url = (
+ subprocess.run(
+ [
+ "gcloud",
+ "run",
+ "services",
+ "describe",
+ service_name,
+ "--platform=managed",
+ f"--project={PROJECT}",
+ f"--region={REGION}",
+ "--format=get(status.url)",
+ ],
+ capture_output=True,
+ )
+ .stdout.decode("utf-8")
+ .strip()
+ )
+
+ logging.info(f"service_url: {service_url}")
+ yield service_url
+
+ # https://cloud.google.com/sdk/gcloud/reference/run/services/delete
+ subprocess.check_call(
+ [
+ "gcloud",
+ "run",
+ "services",
+ "delete",
+ service_name,
+ "--platform=managed",
+ f"--project={PROJECT}",
+ f"--region={REGION}",
+ "--quiet",
+ ]
+ )
+
+
+@pytest.fixture(scope="session")
+def identity_token() -> str:
+ yield (
+ subprocess.run(
+ ["gcloud", "auth", "print-identity-token", f"--project={PROJECT}"],
+ capture_output=True,
+ )
+ .stdout.decode("utf-8")
+ .strip()
+ )
+
+
+@pytest.fixture(scope="session")
+def train_model(bucket_name: str) -> str:
+ aiplatform.init(project=PROJECT, staging_bucket=bucket_name)
+ job = aiplatform.CustomTrainingJob(
+ display_name="climate_script_colab",
+ script_path="task.py",
+ container_uri="us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-7:latest",
+ )
+
+ job.run(
+ accelerator_type="NVIDIA_TESLA_K80",
+ accelerator_count=1,
+ args=[f"--bucket={bucket_name}"],
+ )
+
+ logging.info(f"train_model resource_name: {job.resource_name}")
+
+ # Wait until the model training job finishes.
+ status = None
+ logging.info("Waiting for model to train.")
+ for _ in range(0, TIMEOUT_SEC, POLL_INTERVAL_SEC):
+ # https://googleapis.dev/python/aiplatform/latest/aiplatform_v1/job_service.html
+ status = job.state.name
+ if status in VERTEX_AI_FINISHED_STATE:
+ break
+ time.sleep(POLL_INTERVAL_SEC)
+
+ logging.info(f"Model job finished with status {status}")
+ assert status == VERTEX_AI_SUCCESS_STATE
+ yield job.resource_name
+
+
+def get_prediction_data(lon: float, lat: float, start: str, end: str) -> dict:
+ """Extracts Sentinel image as json at specific lat/lon and timestamp."""
+
+ location = ee.Feature(ee.Geometry.Point([lon, lat]))
+ image = (
+ ee.ImageCollection(IMAGE_COLLECTION)
+ .filterDate(start, end)
+ .select(BANDS)
+ .mosaic()
+ )
+
+ feature = image.neighborhoodToArray(ee.Kernel.square(PATCH_SIZE)).sampleRegions(
+ collection=ee.FeatureCollection([location]), scale=SCALE
+ )
+
+ return feature.getInfo()["features"][0]["properties"]
+
+
+def test_predict(
+ bucket_name: str,
+ test_data: str,
+ train_model: str,
+ service_url: str,
+ identity_token: str,
+) -> None:
+
+ # Test point
+ prediction_data = get_prediction_data(
+ -84.80529, 39.11613, "2021-10-01", "2021-10-31"
+ )
+
+ # Make prediction
+ response = requests.post(
+ url=f"{service_url}/predict",
+ headers={"Authorization": f"Bearer {identity_token}"},
+ json={"data": prediction_data, "bucket": bucket_name},
+ ).json()
+
+ # Check that we get non-empty predictions.
+ assert "predictions" in response["predictions"]
+ assert len(response["predictions"]) > 0