EORST is both an executable binary that can be run, and a library that can be used in Rust programs.

Installing the command-line executable

Assuming you have Rust/Cargo installed, run this command in a terminal:

cargo install eorst

It will make the eorst command available in your PATH if you"ve allowed the PATH to be modified when installing Rust. cargo uninstall eorst uninstalls.

Adding `eorst` library as a dependency

Run this command in a terminal, in your project"s directory:

cargo add eorst

To add it manually, edit your project"s Cargo.toml file and add to the [dependencies] section:

eorst = "0.2.0"

The eorst library will be automatically available globally.

Back to the crate overview.

Readme

An earth observation and remote sensig tookit written in rust.

Summary

This crate offers a library aiming to simplify the writing of raster processing pipelines in rust.

Highlights

Parallel read/write.
On-the-fly image reprojection.
Point raster sampling extraction.
Time series analysis.
Band math.
Basic opencv integrtion
Easy to integrate with LigthGBM and XGBoost.

Crate Status

Still iterating on and evolving the crate.
- The crate is continuously developing, and breaking changes are expected during evolution from version to version.

Details

EORST is an open-source library aiming to fill a void in the rust ecosystem, that is the lack of a library to simplify writing geospatial processing code. It takes inspiration from well-established python libraries such as rasterio, rios, and dask and implements some of them in rust, enabling the programmer to focus on the geospatial process rather than the implementation details while taking advantage of the benefits of the rust programming language.

Despite being in early stages of development eorst already offers a rich set of tools such as simple reading and writing of geospatial data on-the-fly projection and alignment of input data, partition input data into small blocks allowing computation on arrays larger than memory concurrently, efficient raster point sampling, simple band math, time series analysis and integration with 3rd party libraries like such as opencv to perform computer vision task and LightGBM and XGBoost for machine learning. The library is available in the official creates registry https://crates.io/crates/eorst.

The main focus of this library is to support the work of the JRSRP, but I expect it to be useful for a wider audience. Most of the current functionalities were developed to support the Spatial Biocondition project, developed jointly by Quenslands Remote Sensing Centre and the Quensland Herbarium.

Future work in the short term will be focused on the stabilization of the API and extending the documentation and examples, collaboration is welcome.

How to use:

As a library:

Just add eotrs to your dependencies in cargo.toml like:

[dependencies]
eotrs = {git = https://gitlab.com/leohardtke/eotrs"}

As a cli tool

If you want to use the comman line interface see install for instructions.

Examples:

eorst extract data/cemsre_t55jfm_20200614_sub_abam5.tif\
        data/cemsre_t55jfm_20200614_sub_abam5.gpkg\
        id\
        test.csv\
        -b 1,2,3\
        -s 256\
        -n 8

Installing the command-line executable

Adding eorst library as a dependency

Adding `eorst` library as a dependency