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https://github.com/hcording/ecomapper

Segmentation of arbitrary features in very high resolution remote sensing imagery
https://github.com/hcording/ecomapper

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Segmentation of arbitrary features in very high resolution remote sensing imagery

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# Segmentation of arbitrary features in very high resolution remote sensing imagery



![PyPI - Version](https://img.shields.io/pypi/v/ecomapper)



This is the code repository for my M.Sc. thesis at Imperial College London,

"[Segmentation of arbitrary features in very high resolution remote sensing imagery](https://arxiv.org/abs/2412.16046v1)".



The initial code version at time of publication on arXiv is tagged `v1.0.0`. The codebase may evolve in the future with

new features, bugfixes, etc.



## Abstract



Click to expand



Very high resolution (VHR) mapping through remote sensing (RS) imagery presents

a new opportunity to inform decision-making and sustainable practices in

countless domains. Efficient processing of big VHR data requires automated

tools applicable to numerous geographic regions and features. Contemporary RS

studies address this challenge by employing deep learning (DL) models for

specific datasets or features, which limits their applicability across

contexts.



The present research aims to overcome this limitation by introducing EcoMapper,

a scalable solution to segment arbitrary features in VHR RS imagery. EcoMapper

fully automates processing of geospatial data, DL model training, and

inference. Models trained with EcoMapper successfully segmented two distinct

features in a real-world UAV dataset, achieving scores competitive with prior

studies which employed context-specific models.



To evaluate EcoMapper, many additional models were trained on permutations of

principal field survey characteristics (FSCs). A relationship was discovered

allowing derivation of optimal ground sampling distance from feature size,

termed Cording Index (CI). A comprehensive methodology for field surveys was

developed to ensure DL methods can be applied effectively to collected data.



![EcoMapper Architecture](graphical_abstract.jpg)



## Installation



_Please note that EcoMapper is being developed and tested primarily on Linux.

Support for Windows is a future goal, but not currently guaranteed._



Click to expand



First, check if conda is already installed by running:



```shell

conda --version

# Should print: conda 

```



If an error is reported, install `miniconda`

from https://docs.conda.io/en/latest/miniconda.html or use `miniforge`, which provides `mamba` as a

faster alternative:

https://github.com/conda-forge/miniforge



Next, update conda:



```shell

conda update -n base -c conda-forge conda -y

```



Create an environment and activate it. Be sure to use Python __3.10__ as shown.



```shell

conda create -n ecomapper python=3.10 GDAL=3.7.2 -y

conda activate ecomapper

```



Update pip and install the project dependencies:



```shell

pip install --upgrade pip

pip install -r requirements.txt

```



Install MMCV (be sure to use `mim` instead of `pip`!)



```shell

mim install "mmcv==2.0.0"

```



Install MMSegmentation and MMDetection



```shell

pip install git+https://github.com/open-mmlab/mmsegmentation@30a3f94f3e2916e27fa38c67cc3b8c69c1893fe8

pip install git+https://github.com/open-mmlab/mmdetection@ecac3a77becc63f23d9f6980b2a36f86acd00a8a

```



## Usage



Please refer to [the quickstart guide](QUICKSTART.md).



## Development



### Generating documentation



Build modules (only if any modules were changed):



```shell

sphinx-apidoc -M -f -o docs/ ./ecomapper

```



Generate documentation:



```shell

python -m sphinx -b html docs/ docs/_build/

```



After building, you can open the HTML documentation from `docs/_build/index.html`.



### Running tests



Please note that some tests in `tests/functional` are computationally expensive

and may take longer to complete on CPU-only systems. Using a dedicated GPU is recommended.



To run tests:

```shell

pytest

```



### Linting with flake8



```shell

python -m flake8

```



### Type checking with Pytype



```shell

python -m pytype

```



### Regenerating `requirements.txt`



```shell

uv pip compile --universal --output-file=requirements.txt requirements.in

```



Afterwards, sync the requirements:



```shell

uv pip sync requirements.txt



mim install "mmcv==2.0.0"

pip install git+https://github.com/open-mmlab/mmsegmentation@30a3f94f3e2916e27fa38c67cc3b8c69c1893fe8

pip install git+https://github.com/open-mmlab/mmdetection@ecac3a77becc63f23d9f6980b2a36f86acd00a8a



mamba uninstall GDAL -y

mamba install GDAL==3.7.2 -y

```



## License



The repository uses the GPL v3 license. If this is significantly constricting you, please reach out to me!



[License](LICENSE)



## Citation



If you find this work useful, please cite:



DOI: https://doi.org/10.48550/arXiv.2412.16046



Bibtex:



```

@misc{cording2024segmentationarbitraryfeatureshigh,

      title={Segmentation of arbitrary features in very high resolution remote sensing imagery}, 

      author={Henry Cording and Yves Plancherel and Pablo Brito-Parada},

      year={2024},

      eprint={2412.16046},

      archivePrefix={arXiv},

      primaryClass={cs.CV},

      url={https://arxiv.org/abs/2412.16046}, 

}

```



## To-do list



- [ ] Include additional models from MMSegmentation for training

- [ ] Mute MMSegmentation output and write to log file instead

- [ ] Working verbosity; use `logging` for all messages

- [ ] Template generator that can be used to fill out a task and submit it

  (useful for headless HPC environments)

- [ ] Wider test coverage

- [ ] Improved documentation, publish docs on readthedocs