Using the Geo Artificial Intelligence Data Science Virtual Machine
Use the Geo AI Data Science VM to fetch data for analysis, perform data wrangling, and build models for AI applications that consume geospatial information. Once you have provisioned your Geo AI Data Science VM and signed into ArcGIS Pro with your ArcGIS account, you can start interacting with ArcGIS desktop and ArcGis online. You can also access ArcGIS from Python interfaces and an R language bridge pre-configured on the Geo-Data Science VM. To build rich AI applications, combine it with the machine learning and deep learning frameworks and other data science software available on the Data Science VM.
The Python library, arcpy, which is used to interface with ArcGIS is installed in the global root conda environment of the Data Science VM that is found at
- If you are running Python in a command prompt, run
activateto activate into the conda root Python environment.
- If you are using an IDE or Jupyter notebook, you can select the environment or kernel to ensure you are in the correct conda environment.
The R-bridge to ArcGIS is installed as an R library named arcgisbinding in the main Microsoft R server standalone instance located at
C:\Program Files\Microsoft\ML Server\R_SERVER. Visual Studio, RStudio, and Jupyter are already pre-configured to use this R environment and will have access to the
arcgisbinding R library.
Geo AI Data Science VM samples
In addition to the ML and deep learning framework-based samples from the base Data Science VM, a set of geospatial samples is also provided as part of the Geo AI Data Science VM. These samples can help you jump-start your development of AI applications using Geospatial data and the ArcGIS software.
Getting stated with Geospatial analytics with Python: An introductory sample showing how to work with Geospatial data using the Python interface to ArcGIS provided by the arcpy library. It also shows how you can combine traditional machine learning with geospatial data and visualize the result on a map in ArcGIS.
Pixel-level land use classification: A tutorial that illustrates how to create a deep neural network model that accepts an aerial image as input and returns a land-cover label. Examples of land-cover labels are "forested" or "water." The model returns such a label for every pixel in the image. The model is built using Microsoft's open-source Cognitive Toolkit (CNTK) deep learning framework.
Additional samples that use the Data Science VM are available here: