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Low Distortion Map Projections for the Artemis III Candidate Landing Sites
Creation of a survey grade product to support Artemis mission operations and lunar surface science.
Automated accuracy and quality assessment tools (AQAT = “a cat”) for generalized geospatial data
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.
Informing the use of native plant materials in restoration and rehabilitation with the Native Plant Seed Mapping Toolkit
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.
Connecting with our stakeholders - developing a better understanding of use and usability for science products
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.
Integrating stream gage records, water presence observations, and models to improve hydrologic prediction in stream networks
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.
Extracting data from maps: applying lessons learned from the AI for Critical Mineral Assessment Competition
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.
Availability, documentation, & community support for an open-source machine learning tool
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.
Evaluation and recommendation of practices for publication of reproducible data and software releases in the USGS
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.
CorVis: A lidar point cloud tool for visualization and analysis of corridors such as hydrologic, energy, and transportation networks
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.
An open-source interactive time series viewer for geophysical data
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.
Quantifying landcover drivers of urban extreme heat by generating nationwide and city-specific analytical models
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.
Terrain change time machine: creating LiDAR-like historical elevation data
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.