04-05-2017 04:48 AM
We create most of our data products using ERDAS and ESRI proprietary software (as well as OS Python libraries).
Our work many focuses on using Landsat and Sentinel Imagery for regional products, however up until now we have only used services like AWS and Google Engine to download imagery and then automatically process the data in-house.
We are desperately trying to bridge the gap of processing imagery using our own developed models and algorithms in the cloud instead of using our in-house infrastructure. E.g. Google Earth Engine in currently dominating this space, however commercially this is not an option.
Many of our processes are predominantly ERDAS spatial modeler based, and we would like to run those models in the cloud on imagery in the public dataset libraries.
Thanks for your time in advance.
04-07-2017 09:05 AM
Hexagon Geospatial has a collection of Smart M.App offerings that range from the simplicity of M.App Exchange to the complete customizability of M.App X. You should explore those offerings to see if there is one that balances the minimization of your effort with a faithful realization of what you are envisioning as your solution.
With respect to web accessible data, it is true that Spatial Modeler does not, in general, currently allow inputs and outputs to be specified as web accessible resources. Is this what you are expecting? If so, can you elaborate on what URI scheme's and associated mime types (or other web data access standards) you would like to see supported?
I am not aware of any facility to translate one geospatial workflow execution description (such as .gmdx) to another one (such as Google Earth Engine-based java script).
Google App Engine is a Platform as a Service and cloud computing platform for developing and hosting web applications in Google-managed data centers. Applications are sandboxed and run across multiple servers.
Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services.