SCET (Shoreline Change Estimation Toolkit)

A pure-Python toolkit for extracting shorelines from segmented raster imagery and computing shoreline change rates. Transect casting, shoreline/transect intersection, and change-rate calculation are delegated to the OpenDSAS CLI (dsas), a fast, cross-platform reimplementation of USGS DSAS.

Installation

pip install scet-toolkit

This installs scet-toolkit's dependencies including opendsas, which provides the dsas CLI used for transect/intersection/rate calculations.

For the optional deep-learning-based segmentation step (scet.simple_mmseg, used by SCET.method1/method2), install the dl extra:

pip install "scet-toolkit[dl]"

torch/torchvision/torchaudio are CUDA-build-sensitive; if the plain pip install doesn't pick the right wheel for your platform, install them from https://pytorch.org first, matching your CUDA version, then re-run. mmcv-lite (no compiled CUDA ops, unlike mmcv) is used instead of mmcv since this project's segmentation config doesn't need them and mmcv-lite installs via plain pip with no CUDA toolchain required.

Usage

from scet import SCET, Config

config = Config(checkpoint_file="path/to/checkpoint.pth")
scet = SCET(config)
scet.method1(input_naip_zipfiles_folder="...", output_folder="...")

Or drive the shoreline pipeline directly, given a folder of already-segmented, dated raster images (filenames ending in a 4-digit year, e.g. 1_2001.tif):

from scet.options import Options
from scet.pipeline import generate_result_from_folder

generate_result_from_folder("images/", "output/", Options())

This writes shoreline.shp, baseline.shp, transect.shp, and intersection.shp to the output folder.

Development

conda create -n my_env python==3.9
conda activate my_env
bash build.sh  # pip install -e ".[dl]"