CI-Net

This repository contains the CI-Net processing, training, inference, and validation code.

Directory structure

CI-Net/
├── code/
│   ├── data_preparing/
│   ├── labeling/
│   ├── final_preprocess/
│   ├── training/
│   └── validation/
├── raw_data/
└── result/

The directories under code follow the processing order:

1. data_preparing: read and align satellite and radar inputs.
2. labeling: create cloud labels.
3. final_preprocess: convert the prepared fields into model input files.
4. training: train the model and run inference.
5. validation: create validation targets and calculate validation metrics.

Each code directory contains Python files in src and its configuration and shell entry points in run.

raw_data contains the input example included with this repository. result contains prepared data, model files, inference outputs, and validation outputs. See result/README.md for the meaning of its subdirectories.

Environment

conda env create -f environment.yml
conda activate ci-net

Usage

The example preprocessing stages can be run in order:

code/data_preparing/run/run.sh
code/labeling/run/run.sh
code/final_preprocess/run/run.sh

Training and inference use separate configuration files and entry points:

code/training/run/train.sh
code/training/run/inference.sh

Validation target creation and object validation are also separate:

code/validation/run/targets.sh
code/validation/run/validation.sh

The YAML file beside each shell script controls its input and output paths. The shell scripts locate the repository root automatically.

한국어 안내

코드는 data_preparing → labeling → final_preprocess → training → validation 순서로 구성되어 있습니다. 각 단계의 run 폴더에 있는 YAML에서 입출력 경로를 설정한 뒤 셸 파일을 실행하면 됩니다. raw_data에는 예제 입력이, result에는 단계별 결과가 들어 있습니다.

License

The code license is provided in LICENSE_CODE. License information for the model and data files is provided in LICENSE_MODEL_DATA.