Publication

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.

Restoring ecosystems using native plant materials is a critical pursuit of federal land management agencies following natural disasters and disturbances. The Native Plant Seed Mapping Toolkit provides practitioners with quantitative data to support successful restoration outcomes.

The value of USGS tools and products can be assessed by collecting use metrics, user feedback, and examples of practical application. We will pilot an approach to assess the utility of two Coastal Change Hazards product releases and establish a guide for tracking the use and user experience of USGS products.

Develop a process-guided deep learning modeling framework to integrate high-frequency streamflow data from gages, discrete streamflow measurements, surface water presence/absence observations, and streamflow model outputs to improve hydrological predictions on small streams.

This project will share techniques developed in two AI/ML competitions run in Fall 2022, Automated Map Georeferencing, and Automated Map Feature Extraction with USGS stakeholders. We will develop a strategy to operationalize successful approaches, benefiting any activity that uses legacy map data.

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.

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.

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.

To help users connect and comprehend USGS data, we propose to develop an interactive viewer for multi-channel geophysical data using existing Python PyViz tools.

We synthesize local high-resolution urban landcover imagery with microclimate data and regional meteorology to determine landcover drivers of extreme urban heat. Resulting outputs are mappable items spatially describing urban temperatures at fine scales, and a web application to analyze changes in urban heat under different climate scenarios.

This project leverages the USGS's photo archive and Structure from Motion algorithms to derive historical elevation and geomorphic data to catalyze a long-term landscape change analysis of a conservation area. We propose to create a best practices workflow and establish suitable accuracy metrics.

This proposal would fund the testing of quantitative methods for extracting total water level from imagery, with add-on applications including satellite shoreline detection, digital stream gauges, and flood detection. This project supports national scale USGS coastal hazards products.