SEGMENTATION OF WATER BODIES FROM SATELLITE IMAGES (sat-water)

Introduction

Satellite imagery is a rich source of information, and the accurate segmentation of water bodies is crucial for understanding environmental patterns and changes over time. This project aims to provide a reliable and efficient tool for extracting water regions from raw satellite images.

This repository supports two workflows:

1. Library usage: install with pip and run inference (pretrained weights downloaded on-demand).
2. Training workflow: train your own models using the included preprocessing + training pipeline.

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Dataset

The dataset for this project is gotten here kaggle.com. It consists of jpeg images of water bodies taken by satellites and their mask. More details of the dataset is provided on the website.

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Installation

As a library

pip install sat-water

To run inference/training you must install the TensorFlow extras:

pip install "sat-water[tf]"

From source (development)

git clone https://github.com/busayojee/sat-water.git
cd sat-water
pip install -e .

Note: sat-water sets TF_USE_LEGACY_KERAS=1 and SM_FRAMEWORK=tf.keras by default at import time to keep segmentation-models compatible.

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Pretrained models

Pretrained weights are hosted on Hugging Face and downloaded at inference time with SHA256 integrity verification.

Default weights repo:

• busayojee/sat-water-weights

Override weights source:

export SATWATER_WEIGHTS_REPO="busayojee/sat-water-weights"
export SATWATER_WEIGHTS_REV="main"

Available model keys

This project was trained on 2 models. The UNET with no backbone and the UNET with a RESNET34 backbone of which 2 different models were trained on different sizes of images and also different hyperparameters.

Model key	Architecture	Input size	Notes
resnet34_256	UNet + ResNet34 backbone	256×256	Best speed/quality tradeoff
resnet34_512	UNet + ResNet34 backbone	512×512	Higher-res boundaries; slower
unet	UNet (no backbone)	128×128	Currently unavailable in weights repo

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Quickstart (library inference)

from satwater.inference import segment_image

res = segment_image(
    "path/to/image.jpg",
    model="resnet34_512",      # or "resnet34_256"
    return_overlay=True,
    show=False,
)

mask = res.masks["resnet34_512"]         # (H, W, 1)
overlay = res.overlays["resnet34_512"]   # (H, W, 3)

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Inference API

segment_image(...) is the recommended entrypoint for package users.

Parameters (commonly used)

• image_path (str): path to an input image (.jpg, .png, etc.)
• model (str): one of resnet34_256, resnet34_512 (and unet once available)
• return_overlay (bool): whether to return an overlay image (original image + blended water mask)
• show (bool): whether to display the result via matplotlib (useful in notebooks / local runs)

Weights source / versioning

• repo_id (str, optional): Hugging Face repo containing weights (defaults to SATWATER_WEIGHTS_REPO)
• revision (str, optional): branch / tag / commit (defaults to SATWATER_WEIGHTS_REV)
• save_dir (str | Path | None, optional): output directory (if supported in your local version).
  If you want saving, you can always do it manually from the returned arrays (example below).

Manual saving

from PIL import Image
import numpy as np

Image.fromarray((mask.squeeze(-1) * 255).astype(np.uint8)).save("mask.png")
Image.fromarray(overlay).save("overlay.png")

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Training history (reference)

The plots below are from historical runs in this repository and are provided to show convergence behavior.

UNet (baseline)	ResNet34-UNet (256×256)	ResNet34-UNet (512×512)

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Inference examples

Qualitative predictions produced by the three models.

UNet	ResNet34-UNet (256×256)	ResNet34-UNet (512×512)

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Single test instance (end-to-end)

Using all models to predict a single test instance.

Test Image	Prediction

Label overlay of the best prediction (ResNet34-UNet 512×512 in that run):

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Train your own model

Preprocessing

from satwater.preprocess import Preprocess

train_ds, val_ds, test_ds = Preprocess.data_load(
    dataset_dir="path/to/dataset",
    masks_dir="/Masks",
    images_dir="/Images",
    split=(0.7, 0.2, 0.1),
    shape=(256, 256),
    batch_size=16,
    channels=3,
)

Training (UNet baseline)

from satwater.models import Unet

history = Unet.train(
    train_ds,
    val_ds,
    shape=(128, 128, 3),
    n_classes=2,
    lr=1e-4,
    loss=Unet.loss,
    metrics=Unet.metrics,
    name="unet",
)

Training (ResNet34-UNet)

from satwater.models import BackboneModels

bm = BackboneModels("resnet34", train_ds, val_ds, test_ds, name="resnet34_256")
bm.build_model(n_classes=2, n=1, lr=1e-4)
history = bm.train()

For a 512×512 run, load a second dataset with shape=(512, 512) and use a different model name (e.g. resnet34_512) to keep artifacts separate.

Inference

To run inference for UNET

inference_u = Inference(model="path/to/model",name="unet")
inference_u.predict_ds(test_ds)

for RESNET 1 and 2

inference_r = Inference(model="path/to/model",name="resnet34")
inference_r.predict_ds(test_ds)

inference_r2 = Inference(model="path/to/model",name="resnet34(2)")
inference_r2.predict_ds(test_ds1)

For all 3 models together

models={"unet":"path/to/model1", "resnet34":"path/to/model2", "resnet34(2)":"path/to/model3"}
inference_multiple = Inference(model=models)
inference_multiple.predict_ds(test_ds)

CLI (optional)

If you included the scripts/ folder in your package/repo, you can run the scripts directly.

Training CLI

UNet:

python scripts/train.py   --dataset path/to/dataset   --image-folder /Images   --mask-folder /Masks   --shape 128,128,3   --batch-size 16   --split 0.2,0.1   --channels 3   --model unet   --name unet   --epochs 100   --lr 1e-4

ResNet34-UNet (256):

python scripts/train.py   --dataset path/to/dataset   --image-folder /Images   --mask-folder /Masks   --shape 256,256,3   --batch-size 8   --split 0.2,0.1   --channels 3   --model resnet34   --name resnet34_256   --epochs 100   --lr 1e-4

ResNet34-UNet (512):

python scripts/train.py   --dataset path/to/dataset   --image-folder /Images   --mask-folder /Masks   --shape 512,512,3   --batch-size 4   --split 0.2,0.1   --channels 3   --model resnet34(2)   --name resnet34_512   --epochs 100   --lr 1e-4

Inference CLI

Single model:

python scripts/infer.py   --image path/to/image.jpg   --model path/to/model.keras   --name unet   --out prediction

Multiple models:

python scripts/infer.py   --image path/to/image.jpg   --models "unet=path/to/unet.keras,resnet34=path/to/resnet34.keras,resnet34(2)=path/to/resnet34_2.keras"   --out prediction

Upload weights to Hugging Face (optional)

export HF_TOKEN="YOUR_HUGGINGFACE_TOKEN"

python scripts/weights.py   --repo-id user/repo   --hf-root weights   --out-dir dist/weights   --model unet=path/to/unet.keras@128,128,3   --model resnet34_256=path/to/resnet34_256.keras@256,256,3   --model resnet34_512=path/to/resnet34_512.keras@512,512,3

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Contributing

Contributions are welcome — especially around:

• adding/refreshing pretrained weights (including UNet)
• improving inference UX (CLI, batch inference, better overlays)
• expanding tests and CI matrix
• model evaluation and benchmarking on additional datasets

How to contribute

1. Fork the repo
2. Create a feature branch:

   git checkout -b feat/my-change

3. Run checks locally:

   pytest -q
   ruff check .
   ruff format .

4. Open a pull request with a short summary + screenshots (if changing inference output)

If you’re reporting a bug, please include:

• OS + Python version
• TensorFlow version
• full traceback + a minimal repro snippet

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