Data and Information Assets

We will develop a vulnerability assessment R Shiny web application and present to stakeholders. The stakeholder feedback will be summarized into a one page ‘lessons learned’ document that will assist researchers in designing effective climate change visualizations and an R markdown ‘quick start’ guide on R Shiny applications.

This project develops an open-source toolkit for the consistent, automated assessment of accuracy and cartographic quality of generalized geospatial data. The toolkit will aid USGS and other stakeholders with the development and use of multiscale data and with associated decision-making.

This project will combine the 117,000 orphaned oil and gas wells in the USGS Orphaned Well Dataset with groundwater quality data from the USGS National Water Information System (NWIS) to create a data product that can be used to analyze the interactions between orphaned wells, groundwater, and hazards to the environment.

We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.

We aim to develop a web-based game where players use human-assisted image segmentation to produce annotated “meditation drawing” images of surface water sites to accelerate the creation of machine learning imagery training datasets. The game will also public education and outreach opportunities.

In practice, e.g., in model applications, data are rarely complete without workflow code and workflows are often treated as software that include data. This project aims to understand current practice and recommend future practices that better fit the needs of reproducible workflows and models.

We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.

We will develop reproducible workflows in R and Python to combine already existing and underutilized field data collected as part of the USGS streamgage network with remotely sensed data to map flood-prone areas for various recurrence intervals in both gaged and ungaged stream reaches.

This proposal would fund the development of Seg2Map, a new open-source, browser-accessible software deployed on the cloud that will apply Machine Learning to imagery and image time-series, to make highly customizable to study Earth’s changing surface for a range of scientific purposes.

We propose to develop a suite of Jupyter notebooks that leverage existing APIs, cloud storage, and open source tools to make it easier for users to efficiently access USGS 3DEP data and to produce data processing and visualization workflows. These notebooks will enhance data utilization, stimulate creative applications, and generate significant return on investment for 3DEP.

An open-source tool for 3D visualization of lidar point cloud data along a vector line network and output of related lidar metrics. This tool will make available the valuable attribute data of point clouds to enable research such as riparian zone and migration corridor vegetation structure analysis or characterizing the related built environment.

We propose a summit for USGS and Tribal nation partners to share critical knowledge of past and future flooding. The summit will build on USGS research investigating changing flood conditions, improve access to flood information, and aid in building climate resilient communities.