Unlocking the Power of GeoTIFF: Using COG and STAC

Table of Contents

1. Introduction
2. Loading Cloud Optimized GeoTIFF into GMap
3. Using Cloud Optimized GeoTIFF from Any Cloud Provider
4. Getting Started with GMap
5. Using Jupyter Notebook or Jupyter Lab
6. Understanding Cloud Optimized Geotiff
7. Benefits of Cloud Optimized Geotiff
8. Using DigitalGlobe Data for Cloud Optimized Geotiff
9. Loading Multiple Cloud Optimized Geotiff Images in GMap
10. Introduction to Spatial Temporal Asset Catalog (STAC)
11. Understanding the STAC Structure
12. Publicly Available STAC Examples
13. Using Canada Sport Images for STAC
14. Adding STAC Layers to GMap
15. Conclusion

Introduction

Welcome to episode 44 of the Earth Engine Tutorial. In this video, we will learn how to load cloud optimized GeoTIFF (COG) files into GMap. COG files can be hosted on public cloud services like Google Cloud or Amazon Web Services. However, in this tutorial, we will focus on using COG files from any hosted cloud provider. We will provide step-by-step instructions on loading and analyzing COG files using GMap.

Loading Cloud Optimized GeoTIFF into GMap

To load a COG file into GMap, You can visit gmap.org and follow the instructions provided in the tutorial. If you have Jupyter Notebook or Jupyter Lab installed on your computer, you can also download the notebook and run it locally. Once you have the notebook open, you can activate the conda environment and start the Jupyter Notebook server. This will automatically open your browser with the notebook interface.

Using Cloud Optimized GeoTIFF from Any Cloud Provider

While the previous tutorial focused on using COG files hosted on Google Cloud, this tutorial will Show you how to use COG files from any hosted cloud provider. The process remains the same, regardless of the cloud provider you choose. You can follow the steps outlined in tutorial 38 for a detailed explanation of using COG files from any cloud provider.

Getting Started with GMap

To start using GMap, you need to import the required library and Create an interactive map. Once you have the map, you can define a variable and point it to any GeoTIFF file. This file can be accessed using a URL. You can use the get_bounds_for_url() function to determine the bounding box of the GeoTIFF file. This will provide you with the Latitude and longitude coordinates of the lower left and upper right corners of the file.

Using Jupyter Notebook or Jupyter Lab

If you are using Jupyter Notebook or Jupyter Lab, you can execute the code cells in the notebook to load the COG file into GMap. By providing the URL of the COG file, you can add it as a layer to the map. This allows you to Visualize the data without downloading it to your computer. With GMap, you can easily zoom, pan, and analyze the data.

Understanding Cloud Optimized GeoTIFF

Cloud Optimized GeoTIFF is a format that allows you to efficiently access and analyze geospatial data stored in the cloud. It is a regular GeoTIFF file with additional internal information that enables web clients to retrieve data without downloading the entire file. This means you can quickly access and analyze GeoTIFF files hosted in the cloud without the need for extensive data downloads.

Benefits of Cloud Optimized GeoTIFF

Cloud Optimized GeoTIFF offers several advantages for data users. First, it reduces the need for large data downloads, making data access faster and more efficient. Second, it enables streaming of data directly from the cloud, eliminating the need to store large datasets locally. Finally, it allows for easy sharing and collaboration by providing a standardized and accessible format for geospatial data.

Using DigitalGlobe Data for Cloud Optimized GeoTIFF

In this tutorial, we will use data from DigitalGlobe to demonstrate the process of loading COG files into GMap. You can access the data from the provided link and explore the different COG files available. The files are hosted in the cloud, and you can either download them or use the provided URL to stream the data in GMap.

Loading Multiple Cloud Optimized GeoTIFF Images in GMap

GMap allows you to load multiple COG files as layers in the map. This allows you to compare and analyze different datasets simultaneously. You can use the provided URLs for different COG files and add them as layers in GMap. This will enable you to visualize and analyze the data side by side.

Introduction to Spatial Temporal Asset Catalog (STAC)

The Spatial Temporal Asset Catalog (STAC) is a specification that provides a way to describe geospatial data and make it easily discoverable and accessible to users. It allows for querying and retrieval of geospatial information in a standardized format. STAC is becoming increasingly popular in the geospatial community as more and more datasets are being hosted in the cloud.

Understanding the STAC Structure

STAC follows a nested structure, with features and collections containing multiple items and assets. You can think of it as a folder structure where you have subfolders and files. Each item in the STAC represents a geospatial dataset, and it contains metadata and links to the actual data. By following the links, you can access and retrieve the data.

Publicly Available STAC Examples

There are several publicly available STAC catalogs that you can explore to learn more about the format. You can visit the provided links to access different STAC catalogs and search for specific datasets. STAC catalogs provide a wealth of information about the datasets, including metadata, bounding boxes, and spectral bands.

Using Canada Sport Images for STAC

In this tutorial, we will use the Canada Sport Images catalog as an example of a STAC dataset. You can access the catalog from the provided link and explore the nested structure of the data. The catalog contains multiple items, each representing a specific geospatial dataset. By following the links, you can access the metadata and retrieve the data.

Adding STAC Layers to GMap

To load STAC layers into GMap, you can follow a similar process as loading COG files. By providing the URL of the STAC item, you can add it as a layer in GMap. The STAC item represents a specific dataset, and it contains metadata and links to the actual data. By adding STAC layers to GMap, you can visualize and analyze the data without downloading it.

Conclusion

In this tutorial, we have learned how to load cloud optimized GeoTIFF files and STAC datasets into GMap. We have explored the process of loading COG files from any cloud provider and demonstrated the steps using DigitalGlobe data. We have also introduced the STAC format and shown how to add STAC layers to GMap. By leveraging cloud hosting and streaming capabilities, we can access and analyze geospatial data more efficiently.