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https://github.com/karthikraja95/fsdl_deforestation_detection


https://github.com/karthikraja95/fsdl_deforestation_detection

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# FSDL Deforestation Detection






[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://share.streamlit.io/andrecnf/fsdl_deforestation_detection/fsdl_deforestation_detection/dashboard/streamlit_app.py)



[![Dependencies Status](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)](https://github.com/karthikraja95/fsdl_deforestation_detection/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![Security: bandit](https://img.shields.io/badge/security-bandit-green.svg)](https://github.com/PyCQA/bandit)

[![Pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/karthikraja95/fsdl_deforestation_detection/blob/master/.pre-commit-config.yaml)

[![Semantic Versions](https://img.shields.io/badge/%F0%9F%9A%80-semantic%20versions-informational.svg)](https://github.com/karthikraja95/fsdl_deforestation_detection/releases)

[![License](https://img.shields.io/github/license/karthikraja95/fsdl_deforestation_detection)](https://github.com/karthikraja95/fsdl_deforestation_detection/blob/master/LICENSE)



Detecting deforestation from satellite images: a full stack deep learning project






## Description



A deep learning approach to detecting deforestation risk, using satellite images and a deep learning model. We relied on [Planet](https://www.planet.com/) imagery from two [Kaggle](https://www.kaggle.com/) datasets (one from the [Amazon rainforest](https://www.kaggle.com/c/planet-understanding-the-amazon-from-space) and another on [oil palm plantations in Borneo](https://www.kaggle.com/c/widsdatathon2019)) and trained a [ResNet](https://paperswithcode.com/method/resnet) model using [FastAI](https://docs.fast.ai/). For more details, check the following links:



* [Streamlit dashboard for testing the model and exploring the data](https://share.streamlit.io/andrecnf/fsdl_deforestation_detection/fsdl_deforestation_detection/dashboard/streamlit_app.py)



* [Model training notebook in Colab](https://colab.research.google.com/github/karthikraja95/fsdl_deforestation_detection/blob/master/fsdl_deforestation_detection/experimental/FSDL_Final_Model.ipynb)



* [Project management workspace in Notion](https://www.notion.so/Homepage-2ff744c443814f459d80a6e5819226a5)



* [Loom video about the project](https://www.loom.com/share/365d412db3a0474ba46d4fdd7f4c5494)



* [Medium article about this project](https://towardsdatascience.com/detecting-deforestation-from-satellite-images-7aa6dfbd9f61)



This is the result of a group project, made by [André Ferreira](https://andrecnf.com/) and [Karthik Bhaskar](https://www.kbhaskar.com/), for the [Full Stack Deep Learning - Spring 2021 online course](http://fullstackdeeplearning.com/spring2021/).



## Very first steps



### Initial



1. Initialize `git` inside your repo:



```bash

git init

```



2. If you don't have `Poetry` installed run:



```bash

make download-poetry

```



3. Initialize poetry and install `pre-commit` hooks:



```bash

make install

```



4. Upload initial code to GitHub (ensure you've run `make install` to use `pre-commit`):



```bash

git add .

git commit -m ":tada: Initial commit"

git branch -M main

git remote add origin https://github.com/karthikraja95/fsdl_deforestation_detection.git

git push -u origin main

```



### Initial setting up



- Set up [Dependabot](https://docs.github.com/en/github/administering-a-repository/enabling-and-disabling-version-updates#enabling-github-dependabot-version-updates) to ensure you have the latest dependencies.

- Set up [Stale bot](https://github.com/apps/stale) for automatic issue closing.



### Poetry



All manipulations with dependencies are executed through Poetry. If you're new to it, look through [the documentation](https://python-poetry.org/docs/).



Notes about Poetry




Poetry's [commands](https://python-poetry.org/docs/cli/#commands) are very intuitive and easy to learn, like:



- `poetry add numpy`

- `poetry run pytest`

- `poetry build`

- etc






### Makefile usage



[`Makefile`](https://github.com/karthikraja95/fsdl_deforestation_detection/blob/master/Makefile) contains many functions for fast assembling and convenient work.



1. Download Poetry




```bash

make download-poetry

```






2. Install all dependencies and pre-commit hooks




```bash

make install

```



If you do not want to install pre-commit hooks, run the command with the NO_PRE_COMMIT flag:



```bash

make install NO_PRE_COMMIT=1

```






3. Check the security of your code




```bash

make check-safety

```



This command launches a `Poetry` and `Pip` integrity check as well as identifies security issues with `Safety` and `Bandit`. By default, the build will not crash if any of the items fail. But you can set `STRICT=1` for the entire build, or you can configure strictness for each item separately.



```bash

make check-safety STRICT=1

```



or only for `safety`:



```bash

make check-safety SAFETY_STRICT=1

```



multiple



```bash

make check-safety PIP_STRICT=1 SAFETY_STRICT=1

```



> List of flags for `check-safety` (can be set to `1` or `0`): `STRICT`, `POETRY_STRICT`, `PIP_STRICT`, `SAFETY_STRICT`, `BANDIT_STRICT`.






4. Check the codestyle




The command is similar to `check-safety` but to check the code style, obviously. It uses `Black`, `Darglint`, `Isort`, and `Mypy` inside.



```bash

make check-style

```



It may also contain the `STRICT` flag.



```bash

make check-style STRICT=1

```



> List of flags for `check-style` (can be set to `1` or `0`): `STRICT`, `BLACK_STRICT`, `DARGLINT_STRICT`, `ISORT_STRICT`, `MYPY_STRICT`.






5. Run all the codestyle formaters




Codestyle uses `pre-commit` hooks, so ensure you've run `make install` before.



```bash

make codestyle

```






6. Run tests




```bash

make test

```






7. Run all the linters




```bash

make lint

```



the same as:



```bash

make test && make check-safety && make check-style

```



> List of flags for `lint` (can be set to `1` or `0`): `STRICT`, `POETRY_STRICT`, `PIP_STRICT`, `SAFETY_STRICT`, `BANDIT_STRICT`, `BLACK_STRICT`, `DARGLINT_STRICT`, `ISORT_STRICT`, `MYPY_STRICT`.






8. Build docker




```bash

make docker

```



which is equivalent to:



```bash

make docker VERSION=latest

```



More information [here](https://github.com/karthikraja95/fsdl_deforestation_detection/tree/master/docker).






9. Cleanup docker




```bash

make clean_docker

```



or to remove all build



```bash

make clean

```



More information [here](https://github.com/karthikraja95/fsdl_deforestation_detection/tree/master/docker).






## 🛡 License



[![License](https://img.shields.io/github/license/karthikraja95/fsdl_deforestation_detection)](https://github.com/karthikraja95/fsdl_deforestation_detection/blob/master/LICENSE)



This project is licensed under the terms of the `MIT` license. See [LICENSE](https://github.com/karthikraja95/fsdl_deforestation_detection/blob/master/LICENSE) for more details.



## 📃 Citation



```

@misc{fsdl_deforestation_detection,

  author = {Karthik Bhaskar, Andre Ferreira},

  title = {Predicting deforestation from Satellite Images},

  year = {2021},

  publisher = {GitHub},

  journal = {GitHub repository},

  howpublished = {\url{https://github.com/karthikraja95/fsdl_deforestation_detection}}

}

```