Notebook: Geospatial Analytics and AI using BigQuery and Vertex AI

Description

This issue proposes the development of a new notebook that demonstrates how to load GeoJSON, Shapefiles and KML geo data into BigQuery, create visualizations and analysis.

Data Preparation

Load the public dataset located at gs://dataproc-metastore-public-binaries/tree_preservations using BigQuery functionality. You may use the following formats available in the bucket: csv, shapefiles, geojson, kml. Perform feature engineering to derive relevant variables such as species, location, etc Prepare the data for model training using BigQuery and potentially utilize BigQuery Geographic for geospatial manipulations.

Model Training

Utilize Vertex AI to train a suitable prediction model, to predict for example, tree height, or growth. Leverage pre-built algorithms in Vertex AI.

Interpretation and Application

Draw insights from the prediction results. Discuss potential applications for vegetation management systems. Explore the integration of the solution with other Google Cloud services like Cloud Functions for real-time processing.

Resources

• GCS bucket dataset location: gs://dataproc-metastore-public-binaries/tree_preservations
• Public dataset: https://data.yorkopendata.org/dataset/7866ce4e-7b72-4a4c-b59f-869697d91029
• https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions
• https://medium.com/google-cloud/import-gis-data-into-bigquery-8217dc370040
• https://cloud.google.com/bigquery/docs/geospatial-data

Resources

Contributing guidelines: CONTRIBUTING.md

Note: Please refer to the contributing guidelines for detailed instructions on how to contribute to this repository.

This notebook will provide a valuable resource for users interested in applying BigQuery Dataframes, Gemini. We encourage contributions from the community to help develop this notebook.

We appreciate a lot your contribution! :)