Geospatial 2.0 After Office Hours (AoH) Series Roadmap

Cloud computing has been one of the dominant trends in technology in recent years. It has revolutionized how we all work. The availability of on-demand computing resources for processing, storage and software deployment has shifted how many industries conduct and do business. This article and by extension, the “Skilling up for Geo 2.0 is an After Office Hours(AoH) webinar series explores the convergence of GIS and cloud computing environments and how this has impacted GIS professionals.

What is Cloud Computing?

A quick google search describes cloud computing as “on-demand availability of computer system resources, especially in terms of data storage and computing power, without direct active management by the user” Cloud computing. It refers to hosted services delivered over the Internet. These services can be in terms of access to storage, data and programs used over the Internet instead of your own personal computer hard drive or local storage device. These services are offered across the geospatial value chain.

Example of the Geospatial 2.0 value chain sourced from Josh Gilbert’s post 

Inspired by the article Approaching Geospatial 2.0 on the future of GIS. WiGISKe would like to focus on the different verticals with which cloud computing has impacted GIS and moved it into the web. For the After Office Hours (AoH) series, we decided to introduce the series with an overview of all the service models available to GIS user in a cloud computing environment. This overview can give us an understanding of the flexibility that cloud computing environments give GIS users in terms of data computations, mapping and visualizations, spatial analysis (native to the desktop versions) and data sharing. Then based on this framework, we will be running bi-monthly AoH sessions to help our community understand which cloud-based tools and resources to use based on the different functional areas they work in. Cloud computing environments offer three basic service models (Find here an interesting read on the differences): 

  • Software_as_a_service (SaaS).
  • Platform_as_a_service (PaaS). 
  • Infrastructure_as_a_Service (IaaS).

These three basic services models, when adapted for the GIS environment, may look like: 

1.0 GIS_as_a_Service (SaaS).

GIS_as_a_Service may be considered as an extension of the Software-as-a-Service model for GIS solutions. In a SaaS model, a third-party vendor manages everything from application runtimes, OS, data, networking etc. Cloud GIS terms. These applications are accessible through a web interface only on the client-side, thus eliminating the need for any installation. It is applicable in nearly all functional areas of GIS from mapping visualization to spatial analysis. This SaaS-based solution is expected to become a dominant delivery method for geospatial capabilities since it also integrates Business Intelligence. Examples: 

  • ArcGIS Online
  • CartoDB
  • Tableau
  • MapBox Studio
  • Google Earth Engine
  • (mapping visualizations)

2.0 Imagery_as_a_service / Data_as_a_service (SaaS)

Here we would be looking at the availability of large repositories of spatial data. The most common form of spatial datasets includes satellite data. However, in recent years, there has been an unprecedented amount of spatial data generated by the emergence of social media (example twitter) and extensive use of personal devices. Also, the digitization of government records and publication of the same has given rise to a new source of data in the form of government data portals or open data portals. The most common examples of DaaS include:

  • Most common are base-maps available from Openstreetmap. 
  • Earth on AWS – a huge deposit of satellite imagery mainly being used for machine learning algorithms. (see Geo-cloud services)
  • Data repositories with Google Earth Engine & Google Public Data 
  • Data from NASA Earth Explorer
  • Google Public Datasets.

3.0 Platform_as_a_service

PaaS is a service model that allows customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. The consumer can deploy onto the cloud infrastructure consumer-created or acquired applications using programming languages, libraries, services, and tools supported by the provider. The provider controls the underlying cloud infrastructure, including network, servers, operating systems, or storage. In terms of GIS, we can explore the different software package suites that can be deployed on a private server or cloud infrastructure in order to set-up a GIS platform ecosystem. Example: 

4.0 Infrastructure_as_a_Service 

This type of cloud computing service model offers virtualized computing infrastructure over the Internet. The provider facilities processing, storage, networks, and other computing resources for the consumer to run arbitrary software, operating systems and applications while they handle the hardware side of things and delivers those resources over the Internet. This service model can be but is not often used by individual end-users. Instead, third-party vendors deploy their software using this model then offer a SaaS solution to individual end-user, example ArcGIS server on AWS. The biggest providers here include: 

  • AWS.
  • Google Cloud Engine.
  • Microsoft Azure.

Author ~ Nombuyiselo Murage