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Computational Tools and Services

CDI projects tagged with Computational Tools and Services. Computational tools and services include applications, Web services, data discovery tools, models, semantic services and tools, infrastructure, data brokers, and visualization tools.

Filter Total Items: 116

Communicating stream fish vulnerability to climate change

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.
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Communicating stream fish vulnerability to climate change

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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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ZenRiver game concept: accelerating creation of machine learning imagery training datasets using citizen science

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.
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ZenRiver game concept: accelerating creation of machine learning imagery training datasets using citizen science

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.
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A Tool for Rapid-Repeat High-Resolution Coastal Vegetation Maps to Improve Forecasting of Hurricane Impacts and Coastal Resilience

We will develop and publish a stand-alone Python script to produce high-frequency and high-spatial resolution coastal vegetation maps that leverage new Planet 8-band 3m images, USGS CoNED topo-bathy DEMs, and 3DEP Height Above Ground data. These products will help improve forecasts of hurricane impacts.
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A Tool for Rapid-Repeat High-Resolution Coastal Vegetation Maps to Improve Forecasting of Hurricane Impacts and Coastal Resilience

We will develop and publish a stand-alone Python script to produce high-frequency and high-spatial resolution coastal vegetation maps that leverage new Planet 8-band 3m images, USGS CoNED topo-bathy DEMs, and 3DEP Height Above Ground data. These products will help improve forecasts of hurricane impacts.
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Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use

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.
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Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use

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.
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Leveraging Existing USGS Streamgage Data to Map Flood-Prone Areas

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.
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Leveraging Existing USGS Streamgage Data to Map Flood-Prone Areas

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.
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Seg2Map: New Tools for ML-based Segmentation of Geospatial Imagery

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.
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Seg2Map: New Tools for ML-based Segmentation of Geospatial Imagery

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.
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Enhancing usability of 3DEP data and web services with Jupyter notebooks

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.
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Enhancing usability of 3DEP data and web services with Jupyter notebooks

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.
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