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https://github.com/nasaharvest/crop-maml
Learning to predict crop type from heterogeneous sparse labels using meta-learning
https://github.com/nasaharvest/crop-maml
agriculture machine-learning meta-learning remote-sensing
Last synced: about 1 month ago
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Learning to predict crop type from heterogeneous sparse labels using meta-learning
- Host: GitHub
- URL: https://github.com/nasaharvest/crop-maml
- Owner: nasaharvest
- Created: 2021-04-09T01:59:39.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-06-17T18:28:30.000Z (over 3 years ago)
- Last Synced: 2024-08-04T03:11:25.527Z (5 months ago)
- Topics: agriculture, machine-learning, meta-learning, remote-sensing
- Language: Python
- Homepage: https://openaccess.thecvf.com/content/CVPR2021W/EarthVision/html/Tseng_Learning_To_Predict_Crop_Type_From_Heterogeneous_Sparse_Labels_Using_CVPRW_2021_paper.html
- Size: 401 KB
- Stars: 17
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Meta-learning for crop mapping
This repository contains the implementation of [Learning to predict crop type from heterogeneous sparse labels using meta-learning](https://openaccess.thecvf.com/content/CVPR2021W/EarthVision/html/Tseng_Learning_To_Predict_Crop_Type_From_Heterogeneous_Sparse_Labels_Using_CVPRW_2021_paper.html), published at the [EarthVision workshop](http://www.classic.grss-ieee.org/earthvision/dates.html) at CVPR 2021.
## Pipeline
The main entrypoints into the pipeline are [scripts](scripts). Specifically:
* [scripts/export.py](scripts/export.py) exports data (locally, or to Google Drive, depending on what is being exported)
* [scripts/process.py](scripts/process.py) processes the raw data
* [scripts/engineer.py](scripts/engineer.py) combines the earth observation data with the labels to create (x, y) training data
* [scripts/maml.py](scripts/maml.py) trains the MAML model
* [scripts/test.py](scripts/test.py) tests the trained MAML model by finetuning it on the test datasets
* [scripts/ensemble.py](scripts/ensemble.py) takes weights saved by [test.py](scripts/test.py) and ensembles them to create maps
* [scripts/pretrain.py](scripts/pretrain.py) trains a model on all data, for a transfer learning baseline
Two crop type maps created using few positive labelled points are available on [Google Earth Engine](https://code.earthengine.google.com/39a0fedfc7ac7f21c3dcb06eab29917d):
* [Coffee map for 2019-2020 season in Luís Eduardo Magalhães municipality, Brazil](https://code.earthengine.google.com/6d348205d0313a0fdf1ebeaf14edd359)
* [Common bean map for 2019-2020 season in Busia, Kenya](https://code.earthengine.google.com/7ebf03937d5c376dd657dba1d881e789)
### Replicating experiments in the paper
Note: not all datasets used are public, so results cannot be exactly replicated.
1. Download the [LEM+](https://www.sciencedirect.com/science/article/pii/S2352340920314359) dataset, and save it in `data/raw/lem_brazil`
2. Export the GeoWiki labels, by running `export_geowiki` in `scripts/export.py`
3. Process all the labels, by running `scripts/process.py`
4. Export the Sentinel Earth Engine tif files by running the other functions in `scripts/export.py`
5. Combine the labels and raw satellite imagery into `(X, y)` training data by running `scripts/engineer.py`
6. Train the MAML model by running `maml.py`. The MAML model and training results will be saved in `data/maml_models/version_`, where VERSION increments for each MAML run.
7. Finetune 10 MAML model with the following commands, bootstrapping the training data each run: (adding `--test_mode {pretrained, random}` will train the baseline models)
```bash
python maml_test.py --version --dataset Togo --many_n --num_cv 10 # Finetune on the Togo data across varying sample sizes
python maml_test.py --version --dataset coffee --num_samples {-1, 40} --num_cv 10 # Finetune on the coffee dataset for all negative samples, or 20 positive and 20 negative samples
python maml_test.py --version --dataset common_beans --num_samples {-1, 64}, --num_cv 10 # Finetune on the common beans dataset for all negative samples, or 32 positive and 32 negative samples
```
## Setup
[Anaconda](https://www.anaconda.com/download/#macos) running python 3.6 is used as the package manager. To get set up
with an environment, install Anaconda from the link above, and (from this directory) run
```bash
conda env create -f environment.yml
```
This will create an environment named `landcover-mapping` with all the necessary packages to run the code. To
activate this environment, run
```bash
conda activate landcover-mapping
```
#### Earth Engine
Earth engine is used instead of sentinel hub, because it is free. To use it, once the conda environment has been activated, run
```bash
earthengine authenticate
```
and follow the instructions. To test that everything has worked, run
```bash
python -c "import ee; ee.Initialize()"
```
Note that Earth Engine exports files to Google Drive by default (to the same google account used sign up to Earth Engine).
Running exports can be viewed (and individually cancelled) in the `Tabs` bar on the [Earth Engine Code Editor](https://code.earthengine.google.com/).
For additional support the [Google Earth Engine forum](https://groups.google.com/forum/#!forum/google-earth-engine-developers) is super
helpful.
#### Tests
The following tests can be run against the pipeline:
```bash
pytest # unit tests, written in the test folder
black . # code formatting
```
#### Reference
If you find this code useful, please cite the following paper:
```
@InProceedings{Tseng_2021_CVPR,
author = {Tseng, Gabriel and Kerner, Hannah and Nakalembe, Catherine and Becker-Reshef, Inbal},
title = {Learning To Predict Crop Type From Heterogeneous Sparse Labels Using Meta-Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021},
pages = {1111-1120}
}
```