https://github.com/aazuspan/landsat-bigquery

Summarizing 51 years of Landsat data using Earth Engine and BigQuery
https://github.com/aazuspan/landsat-bigquery

bigquery google-earth-engine landsat

Last synced: 9 days ago
JSON representation

Summarizing 51 years of Landsat data using Earth Engine and BigQuery

• Host: GitHub
• URL: https://github.com/aazuspan/landsat-bigquery
• Owner: aazuspan
• Created: 2024-09-16T17:33:59.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-09-16T18:01:36.000Z (5 months ago)
• Last Synced: 2024-11-21T04:02:43.359Z (2 months ago)
• Topics: bigquery, google-earth-engine, landsat
• Language: Python
• Homepage: https://www.aazuspan.dev/blog/summarizing-51-years-of-landsat-data/
• Size: 2.19 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Summarizing Landsat with BigQuery

This is a demo project using the [Earth Engine-BigQuery connector](https://cloud.google.com/blog/products/data-analytics/new-bigquery-connector-to-google-earth-engine) to export every Landsat scene to a BigQuery table where they can be queried, summarized, and visualized to create the animation below.

https://github.com/user-attachments/assets/d90b4773-f842-4250-bbe9-32036de65af0

Check out the accompanying [blog post](https://www.aazuspan.dev/blog/summarizing-51-years-of-landsat-data/) for more details.

## Setup

Install Python dependencies from PyPI.

```bash

python -m venv .venv && source .venv/bin/activate

pip install -r requirements.txt

```

You'll need `gcloud` to authenticate BigQuery. If you have `snap`, you can install it with:

```bash

sudo snap install google-cloud-cli --classic

```

Otherwise, follow the instructions [here](https://cloud.google.com/sdk/docs/install).

Then run:

```bash

gcloud init && gcloud auth application-default login

```

Set the following in `src/config.py`:

- `CLOUD_PROJECT`: The name of the Google Cloud Project to store tables under.

- `DATASET_ID`: The name of the BigQuery dataset that you manually created in that cloud project.

- `TABLE_ID`: The name of the table that will be generated on export.

## Usage

1. Run `python export.py` to generate the BigQuery table with all Landsat scenes. Wait for the task to complete.

1. Run `python queries.py` to execute queries and generate outputs.