robustraster - A Python Software Package To Lower the Barrier of Entry for Large-Scale Geospatial Analysis
type: CreativeWork
description: In recent years, we have witnessed a substantial increase in geospatial data availability and size, predominantly derived from automated high-resolution remote sensing instruments. As our capacity to generate geospatial data continues to expand, we confront challenges in effectively analyzing these datasets due to their sheer size. Consequently, there arises a pressing need for data-intensive computing solutions, such as cloud computing and high-performance computing (HPC) environments. Notably, cloud-based geospatial APIs like Google Earth Engine (GEE) have emerged as a formidable solution to this problem, offering access to petabytes of satellite imagery and a suite of analytical tools, all while removing the concerns regarding data storage and computational resources. However, despite the capabilities of GEE, it remains constrained in the scope of analyses it can facilitate on its data catalog. In response to this limitation, I have developed robustraster, a Python package designed to execute user-defined functions in parallel on massive geospatial datasets using a user’s computational infrastructure, regardless of the size. This package aims to empower geospatial data scientists who may lack familiarity with data-intensive computing solutions and complex data structures.
abstract: In recent years, we have witnessed a substantial increase in geospatial data availability and size, predominantly derived from automated high-resolution remote sensing instruments. As our capacity to generate geospatial data continues to expand, we confront challenges in effectively analyzing these datasets due to their sheer size. Consequently, there arises a pressing need for data-intensive computing solutions, such as cloud computing and high-performance computing (HPC) environments. Notably, cloud-based geospatial APIs like Google Earth Engine (GEE) have emerged as a formidable solution to this problem, offering access to petabytes of satellite imagery and a suite of analytical tools, all while removing the concerns regarding data storage and computational resources. However, despite the capabilities of GEE, it remains constrained in the scope of analyses it can facilitate on its data catalog. In response to this limitation, I have developed robustraster, a Python package designed to execute user-defined functions in parallel on massive geospatial datasets using a user’s computational infrastructure, regardless of the size. This package aims to empower geospatial data scientists who may lack familiarity with data-intensive computing solutions and complex data structures.
author: Matos, Adriano
language: en_US
language: English
date published: 2026
date modified: 2026-07-27T21:46:08Z
provider: University of Nevada - Reno
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Nevada - Reno