The doodleverse is an opinionated collection of Python packages designed for geoscientific image segmentation.

Package maintainers:

• @dbuscombe-usgs
• @ebgoldstein

Currently, there are 3 packages:

• For fast, interactive segmentation of imagery
• For creation of training datasets for training Machine-Learning-based image segmentation models, for example Segmentation Gym

• A neural gym for training image segmentation models based on fully convolutional models based on UNets
• This repository is where you train models based on your own imagery and label data, and evaluate those models

• A repository of pre-trained Gym models for some common tasks
• A set of notebooks that illustrate how best to use a model on sample imagery
• You are encouraged to contribute your Gym models for the common good!

Packages are compatible, and share an underlying design and data structures

• A port of Dash-Doodler using the Anaconda/Holoviews API for graphical user interface
• Dash-Doodler and Holo-Doodler both use the same codes for image label generation, found in doodler_engine repo

• A mapping extension for application of Segmentation Zoo models on geospatial imagery
• Uses codes housed in s2mengine for conversion of label images (from Gym and Doodler) into geospatial formats, and for visualization of generic label images in geospatial formats in a small webGIS viewer

• A mapping extension for CoastSat using Segmentation Zoo models
• Provides a graphical browser-based environment for application of the CoastSat workflow for shoreline mapping
• Provides a graphical browser-based environment for application of Segmentation Zoo models for mapping features in satellite imagery

• Coast Train is a dataset made using Doodler, and has been used for training Zoo models that are being implemented in CoastSeg, Seg2Map, and elsewhere
• The Coast Train repo is here
• Consult the Coast Train website for more info. Data may be accessed from here

• PING-Mapper is a Python interface for reading, processing, and mapping side scan sonar data from Humminbird® sonar systems
• V2 (in development) will use Gym models to identify riverbed substrates, for the purposes of geomorphic and ecological mapping
• Check out the repo and website for more info

• collect beach grain size information using citizen scientists armed with their cell phone and any US coin
• uses a Gym model to detect the coin, so a scaling for the image can be determined for accurate grain size measurements
• a demo of that model is available
• Check out the data and website for more info

Opinionated Software is a software product that encodes a specific way of approaching a task. The Doodleverse is a well-researched end-to-end data-model pipeline for geoscientific image segmentation that has a lot of practices baked into it. In particular, the Doodleverse implements a specific data-creation and model-training pipeline. See this link and this article for more reading around this topic.