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Data Release

Filter Total Items: 33

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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Linking orphaned oil & gas wells with groundwater quality

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.
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Linking orphaned oil & gas wells with groundwater quality

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.
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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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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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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.
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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.
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Enhancing Decision Support with Restoration Project Data Pipelines

Effectively documenting and distributing information about restoration projects is essential for measuring progress towards national conservation goals. We will improve the National Fish Habitat Partnership Project Tracking database by creating a data pipeline to compile project information and link data with other decision support tools.
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Enhancing Decision Support with Restoration Project Data Pipelines

Effectively documenting and distributing information about restoration projects is essential for measuring progress towards national conservation goals. We will improve the National Fish Habitat Partnership Project Tracking database by creating a data pipeline to compile project information and link data with other decision support tools.
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Separating the land from the sea: image segmentation in support of coastal hazards research and community early warning systems

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.
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Separating the land from the sea: image segmentation in support of coastal hazards research and community early warning systems

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.
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Coast Train: Massive Library of Labeled Coastal Images to Train Machine Learning for Coastal Hazards and Resources

Scientists who study coastal ecosystems and hazards such as hurricanes, flooding, and cliff failure collect lots of photographs of coastal environments from airplanes and drones. A large area can be surveyed at high resolution and low cost. Additionally, satellites such as Landsat have provided imagery of the Nation’s coastlines every few days for decades. Scientist’s ability to understand coastal
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Coast Train: Massive Library of Labeled Coastal Images to Train Machine Learning for Coastal Hazards and Resources

Scientists who study coastal ecosystems and hazards such as hurricanes, flooding, and cliff failure collect lots of photographs of coastal environments from airplanes and drones. A large area can be surveyed at high resolution and low cost. Additionally, satellites such as Landsat have provided imagery of the Nation’s coastlines every few days for decades. Scientist’s ability to understand coastal
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Improving forest structure mapping and regeneration prediction with multi-scale lidar observations

To make informed decisions, land managers require knowledge about the state of the ecosystems present. Vegetation structure is a key indicator of the state of forested systems; it influences habitat suitability, water quality and runoff, microclimate, and informs wildfire-related characteristics such as fuel loads, burn severity, and post-fire regeneration. Field data used to derive vegetation st
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Improving forest structure mapping and regeneration prediction with multi-scale lidar observations

To make informed decisions, land managers require knowledge about the state of the ecosystems present. Vegetation structure is a key indicator of the state of forested systems; it influences habitat suitability, water quality and runoff, microclimate, and informs wildfire-related characteristics such as fuel loads, burn severity, and post-fire regeneration. Field data used to derive vegetation st
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