This package provides functionalities to apply statistical and machine learning models to raster and image layers.
The aim is ease of use to develop and test models with spatial raster data. For instance, sklearn models or functions are applied on pixel by pixel basis. Rather than loading raster datasets in memory with python arrays, the package avoids memory issues by processing chunk by chunk (blocks) raster images. This allows for modeling of large raster without memory bottleneck.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Current version, version supports application of:
- sklearn models using simple function (rasterPredict).
Future iterations will include implementation of rasterApply for user function (e.g. pixel based time series functionalities such as FFT).
What things you need to install the software and how to install them
numpy,
sklearn,
geopandas
A step by step series of examples that tell you how to get a development env running
Say what the step will be
pip install rastermodel
End with an example of getting some data out of the system or using it for a little demo
Explain how to run the automated tests for this system
Explain what these tests test and why
Give an example
Explain what these tests test and why
Give an example
Add additional notes about how to deploy this on a live system
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For the versions available, see the tags on this repository.
See also the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE.md file for details
- Hat tip to anyone whose code was used
- Inspiration
- etc