I was recently at University meeting where we got into the discussion of using Cloud Computing for Digital humanities.
Digital Humanities is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities. It includes the systematic use of digital resources in the humanities, as well as the reflection on their application.
Digital Humanities can be defined as new ways of doing scholarship that involve collaborative, transdisciplinary, and computationally engaged research, teaching, and publishing. It brings digital tools and methods to the study of the humanities with the recognition that the printed word is no longer the main medium for knowledge production and distribution.
The Availability of geo-spatial analytics and AI extension to the Microsoft Data Science Virtual Machin is now available via the Azure Marketplace and Data Science Virtual Machine allows Digital Humanities users to use new applications and techniques,
With the tools and services its makes new kinds of teaching and research possible, while at the same time studying and critiquing how these impact cultural heritage and digital culture. The use of the DSVM and ESRI solutions allows the field to both employs technology in the pursuit of humanities, research, and subjects technology to humanistic questioning and interrogation, often simultaneously.
The Geo AI Data Science VM is an extension to the Windows Server 2016 edition of the Microsoft Data Science Virtual Machine (DSVM) on Azure, offered through the collaboration between Esri and Microsoft. https://www.esri.com/en-us/landing-page/lp/product/2018/geo-ai
The Microsoft DSVM contains popular tools for data science as well as AI tools, such as enterprise grade R and Python on the Microsoft Machine Learning Server, Anaconda Python, JuliaPro, Jupyter Notebook for Python, Julia and R, Visual Studio Community edition with Python and R Tools, SQL Server Developer edition, standalone instance of Apache Spark, deep-learning frameworks like TensorFlow, Microsoft Cognitive Toolkit, and several other data science tools and machine learning algorithms. See a comprehensive list of Microsoft DSVM tools and algorithms.
ArcGIS Pro is Esri’s next-gen 64-bit desktop geographic information system (GIS). Technologically ahead of everything else on the market, ArcGIS Pro provides professional 2D and 3D mapping in an intuitive user interface. ArcGIS Pro is a big step forward in advancing visualization, analytics, image processing, data management, and integration.
The Geo AI Data Science VM augments the Microsoft DSVM with rich geo-spatial capabilities of Esri’s ArcGIS Pro. Python and R interfaces to ArcGIS Pro are pre-configured on the Geo AI Data Science VM, enabling programmatic access to geo-spatial analytics within your AI applications out of the box. We also provide samples in the form of Jupyter notebooks to help you start building AI applications infused with geo-spatial intelligence.
If you are building deep learning models on the Geo AI Data Science VM, we recommend you use Azure NC-Series GPU VM instances which is available in select Azure regions. Check here for availability of various services by Azure regions.
AI for Earth sample: https://github.com/Azure/pixel_level_land_classification Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
Introduction to the Geo Artificial Intelligence Data Science Virtual Machine – Azure https://docs.microsoft.com/en-gb/azure/machine-learning/data-science-virtual-machine/geo-ai-dsvm-overview
Using the Geo Artificial Intelligence Data Science Virtual Machine - Azure https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/use-geo-ai-dsvm
How to use a Geo AI Virtual Machine on Azure. http://aka.ms/dsvm/geoai