Geology topics

Sharon Fitzpatrick

Publications and source records attributed to Sharon Fitzpatrick.

3 recordsLinked to original sources

USGS2024

CoastSeg: An accessible and extendable hub for satellite-derived-shoreline (SDS) detection and mapping

CoastSeg is an interactive browser-based program that aims to broaden the adoption of satellite-derived shoreline (SDS) detection and coastal landcover mapping workflows among coastal scientists and coastal resource management practitioners. SDS is a sub-field of coastal sciences that aims to detect and post-process a time-series of shoreline locations from publicly available satellite imagery. CoastSeg is a python package installed via pip into a conda environment that serves as an API for building custom SDS workflows. CoastSeg also provides full SDS workflow implementations via jupyter notebooks and python scripts that call functions and classes in the core CoastSeg API for specific workflows. Two fully functioning SDS workflows are already provided, and more could be added by collaborators in the SDS software community. All API codes, notebooks, scripts, and documentation are hosted on the CoastSeg GitHub repository.

Journal of Open Source Software↗

USGS2023

A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments

The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

Scientific Data↗

USGS2022

Human-in-the-Loop segmentation of earth surface imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed-pixels, -textures, and -spectral signatures, can be especially error-prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi-automated, method for interactive segmentation of N-dimensional (x, y, N) images into two-dimensional (x, y) label images. It uses human-in-the-loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non-photographic imagery consist of 1- and 3-bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.

Earth and Space Science↗