Scaling Computational Gis Workflows To Handle Massive Geodata

Managing Massive Geospatial Datasets

The exponential growth of geospatial data presents new challenges in storing, processing, analyzing, and visualizing large datasets. With terabytes of aerial imagery, lidar scans, satellite data, and vector data being generated daily, traditional desktop Geographic Information Systems (GIS) software can no longer handle the massive volume and velocity of geospatial big data.

To work with massive geospatial datasets, it is necessary to leverage cloud computing infrastructure for storage, and high performance and distributed computing paradigms for analysis. This allows computational GIS workflows to scale elastically to handle large workloads in a timely and cost-efficient manner.

Storing Large Geospatial Datasets in the Cloud

The cloud offers virtually unlimited storage capacity for housing big geospatial data. Cloud object storage services like Amazon S3, Azure Blob Storage, and Google Cloud Storage allow terabytes of imagery and lidar point clouds to be stored cost-effectively in native formats.

Vector and tabular geospatial data can also be held in cloud-hosted spatial databases like Amazon Redshift, Azure SQL Database, and Google BigQuery for analysis using SQL. Cloud data lakes on Amazon S3 or Azure Data Lake Storage provide limitless storage for unstructured and semi-structured data.

Key considerations when storing big geospatial data in the cloud include data transfer mechanisms, access control policies, metadata management, and optimization for analysis. Locality of data to compute is also important for reducing latency when processing analytics workloads.

Processing Big Geospatial Data in a Cluster Environment

To analyze massive geospatial datasets requires leveraging the parallel processing capabilities of cloud-based compute clusters or on-premise HPC infrastructure. Big geospatial data analytics workflows can be run distributively using containers, reducing time-to-insight from days to minutes.

High concurrency computation frameworks like Hadoop, Spark, and Dask enable the sharding of geospatial analysis tasks across many nodes to achieve speed and scale. Containers and Kubernetes provide computational reproducibility and simplify environment configuration for distributed GIS processing.

Careful benchmarking should be undertaken when designing cluster-based geospatial data pipelines to identify optimal configurations for maximizing analysis throughput without overprovisioning cloud resources and incurring unnecessary costs.

Example Code for Distributed Geospatial Analysis on a Cluster

This Python code performs a distributed raster analysis on geospatial data stored in Cloud Storage using Dask and saves the output raster to Cloud Storage:

import dask
import dask.distributed
from dask_cloudprovider import FargateCluster
from import COGReader
from rasterio.plot import show 

# Create a Dask cluster
cluster = FargateCluster()
client = Client(cluster)

# Read geospatial raster dataset as Cloud Optimized GeoTIFF
src_raster = COGReader("s3://bucket/image.tif") 

# Lazy load raster into Dask array for distributed processing 
raster_data = src_raster.ndarray()

# Apply raster calculation using Dask map_overlap 
output = dask.array.map_overlap(lambda x: x.astype(float) * 2, 
                                depth={0: 5000})

# Compute in parallel on the Dask cluster
output = output.compute()  

# Save processed raster to Cloud Storage 
with's3://bucket/output.tif', 'w', **src_raster.profile) as dst:

This demonstrates how Dask can scale geospatial analyses across a cluster to handle large raster datasets stored in the cloud, avoiding memory limitations of desktop GIS tools.

Visualizing the Results of Large-Scale Geocomputation

Visualization of analytic outputs helps users interpret computational geospatial processes and identify data quality issues. However, traditional GIS visualization struggle with big data.

Cluster computing engines like Dask, Spark, and Hadoop integrate with cloud-optimized data formats like Zarr, Cloud Optimized GeoTIFFs (COG), and Parquet for analyzing large arrays, imagery, and tabular data. SQL interfaces in cloud data warehouses also enable visualization.

Data cubes can be generated to aggregate results over space and time for interactive analysis. Downsampling or indexing methodologies help constrain big datasets for responsive visualization. GPU-acceleration leverages hardware performance for rendering tasks.

Emerging web standards like OGC API Features allow cloud-native access to processed analytic outputs while lightweight JavaScript libraries like Deck.GL can provide browser-based visualization of computational geospatial results.

Best Practices for Working With Massive Geospatial Datasets

When handling massive geospatial datasets, it is important to design scalable and flexible cloud architectures, optimize data formats for analysis, leverage high performance computing paradigms, and use open standards for interoperability.

Critical things to consider include:

  • Staging datasets close to cloud compute for reduced data transfer latency when processing
  • Scaling storage and compute resources independently to match workload demands
  • Using open big data formats like Parquet and Zarr for efficient analytic I/O
  • Benchmarking hardware configurations for optimal price-performance balance
  • Automating compute clusters for regular and reproducible workflows
  • Supporting emerging cloud-first standards like STAC, OGC API, etc.

By following modern best practices anchored on cloud infrastructure, even massive geospatial datasets with billions of records and terabytes in size can fuel advanced computational geoanalytics at scale.

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