How can you efficiently process large-scale geospatial data?

My experience with going to the cloud to process extremely large-scale geospatial data is that the cloud its 3-6 times more expensive then doing it locally on my own machines (purchased a 10 years old Index Xeon 54 cores / 128GB RAM / SSD storage for around 3K) and I can import the OpenStreetMap Planet Database without problems. So think of what are the things that you need and see if you can do a hibrid wiere you have your own infrastructure for processing some of the data and you leverage the cloud just for display purposes, doing some queries and do the heavy lifting on your own hardware.

It's all correct, but before dumping everything to the cloud, I would recommend to try and optimize the queries, and the commands in general. Streamlining locally is the first step to deploy successfully to the cloud and saving resources (time, money, energy, and emissions)!

Processing large-scale geospatial data (GD) efficiently using cloud computing (CC) involves leveraging the vast computing resources and specialized services that cloud providers offer. The followings might be useful in this regard. -Consider cloud providers offering specialized services for GD processing like AWS, Google Cloud, or Azure. -Employ databases optimized for GD (e.g., Amazon Aurora with PostgreSQL compatibility, Google Cloud SQL, Azure Cosmos DB with spatial features, or managed instances of PostGIS) - Use specialized GD services (e.g., GEE, AWS Lambda with geospatial libraries) for analysis and manipulation. - Incorporate geospatial analytics tools compatible with cloud instances (e.g., ESRI ArcGIS on AWS, QGIS on cloud VMs).

Besides using specialized indexes (GIST works well out of the box), another powerful PostGIS technique I am a fan of is CLUSTERing the most demanding table on the spatial index. In several occasions the improvements were in the order of 1000 times. Times, not percent!

Vector tiles work like a charm to stream large vector datasets. A tool one can easily deploy to convert existing vector datasets into vector tilesets are: - "PMTiles" which creates eponymous PMtile files - "openmaptiles" based on OpenStreetMap - "tilemaker" to work with OSM files - "Planetiler" creates MBTiles (sqlite) and PMTiles - "tippecanoe", an unconventional name for the swiss knife of vector tiles (unmaintained).

A lot of my geospatial workflows use either global scale or high resolution EO raster data. To get and analyse these, I use R and RStudio with the phenomenally powerful packages terra, sf and sometimes even data.table. The key to success when you're working on your own machine is chunking your data, which is easy for raster data, and then working with these chunks from start to finish. You can even parallelise lots of processes on raster data chunks. Several global data repos now use STAC, a standard to describe data sufficiently to allow the download of subsets. From there on, you can use APIs to download exactly what you need, then run your analysis. Scripts also makes research reproducible, which is great for academic and corporate work!

Processing large-scale data with ML requires ensuring efficiency, accuracy, and scalability. Some key aspects include: - Choosing the Right Machine Learning Models - Distributed Processing - Scalable Inference - Continuous Learning - Integration with cloud computing - Model evaluation for the most optimal solution Adapting to the specific requirements of your geospatial data and use case, you can effectively use machine learning to extract valuable insights from large-scale spatial datasets.

To answer this question, think about what makes data large-scale: if it's tabular (let's use that to simplify the discussion), it has lots of rows and/or columns. If it's a geodatabase, that can be compounded by many tables. I like to keep in mind TRC: tables * rows * columns. The product of that in some sense will influence efficiency in one core way: sheer volume. I can trim processing time independent of my code if I can reduce the product TRC. In essence, what do I need right now? Am I carrying dead weight in any of those dimensions? If it's a regional problem, I might be carrying extra rows. If I have lots of attributes, many of them may be irrelevant. Similarly, a lot of layers of the geodatabase might not be useful. All reduce TRC.