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Inventories of landslides and liquefaction triggered by major earthquakes are key research tools that can be used to develop and test hazard models. To eliminate redundant effort, we created a centralized and interactive repository of ground failure inventories that currently hosts 32 inventories generated by USGS and non-USGS authors and designed a pipeline for adding more as they become available. The repository consists of (1) a ScienceBase community page where the data are available for download and (2) an accompanying web application that allows users to browse and visualize the available datasets. We anticipate that easier access to these key datasets will accelerate progress in earthquake-triggered ground...
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Deep learning is a computer analysis technique inspired by the human brain’s ability to learn. It involves several layers of artificial neural networks to learn and subsequently recognize patterns in data, forming the basis of many state-of-the-art applications from self-driving cars to drug discovery and cancer detection. Deep neural networks are capable of learning many levels of abstraction, and thus outperform many other types of automated classification algorithms. This project developed software tools, resources, and two training workshops that will allow USGS scientists to apply deep learning to remotely sensed imagery and to better understand natural hazards and habitats across the Nation. The tools and...
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Insect pests cost billions of dollars per year globally, negatively impacting food crops and infrastructure and contributing to the spread of disease. Timely information regarding developmental stages of pests can facilitate early detection and control, increasing efficiency and effectiveness. To address this need, the USA National Phenology Network (USA-NPN) created a suite of “Pheno Forecast” map products relevant to science and management. Pheno Forecasts indicate, for a specified day, the status of the insect’s target life cycle stage in real time across the contiguous United States. These risk maps enhance decision-making and short-term planning by both natural resource managers and members of the public. ...
2012 Updates (from the FY12 Annual Review) The NWIS Web Services Snapshot represents the next generation of data retrieval and management. The newest Snapshot tool allows instant access to NWIS data from four different web services through ArcGIS, software available to all USGS scientists in all mission areas. Increased data retrieval efficiency reduces the steps required to retrieve and compile water data from multiple sites from what can be more than 30 steps to just a few clicks. As an end-user education tool, it promotes use of NWIS data from both web services and the NWIS database, which increases the production of scientific research and analysis that uses NWIS data. The Snapshot database design enables efficient...
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Advances in information technology now provide large volume, high-frequency data collection which may improve real-time biosurveillance and forecasting. But, big data streams present challenges for data management and timely analysis. As a first step in creating a data science pipeline for translating large datasets into meaningful interpretations, we created a cloud-hosted PostgreSQL database that collates climate data served from PRISM (https://climatedataguide.ucar.edu/climate-data) and water-quality data from the National Water Quality Portal (https://www.waterqualitydata.us/) and NWIS (https://waterdata.usgs.gov/nwis; fig 1). Using Python-based code, these data streams are queried and updated every 24 hours,...
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Geotagged photographs have become a useful medium for recording, analyzing, and communicating Earth science phenomena. Despite their utility, many field photographs are not published or preserved in a spatial or accessible format—oftentimes because of confusion about photograph metadata, a lack of stability, or user customization in free photo sharing platforms. After receiving a request to release about 1,210 geotagged geological field photographs of the Grand Canyon region, we set out to publish and preserve the collection in the most robust (and expedient) manner possible (fig. 6). We leveraged and reworked existing metadata, JavaScript, and Python tools and developed a toolkit and proposed workflow to display...
Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This project is one of the first attempts to apply AI towards fish population assessment with citizen science. Principal Investigator : Nathaniel P Hitt Co-Investigator : Natalya I Rapstine, Mona (Contractor) Arami, Jeff T Falgout, Benjamin Letcher, Nicholas Polys Cooperator/Partner : Sophia Liu, Fraser Hayes, Ky Wildermuth, Bryan...
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Executive Summary Traditionally in the USGS, data is processed and analyzed on local researcher computers, then moved to centralized, remote computers for preservation and publishing (ScienceBase, Pubs Warehouse). This approach requires each researcher to have the necessary hardware and software for processing and analysis, and also to bring all external data required for the workflow over the internet to their local computer. To explore a more efficient and effective scientific workflow, we explored an alternate model: storing scientific data remotely, and performing data analysis and visualization close to the data, using only a local web browser as an interface. Although this environment was not a good fit...
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Land-use researchers need the ability to rapidly compare multiple land-use scenarios over a range of spatial and temporal scales, and to visualize spatial and nonspatial data; however, land-use datasets are often distributed in the form of large tabular files and spatial files. These formats are not ideal for the way land-use researchers interact with and share these datasets. The size of these land-use datasets can quickly balloon in size. For example, land-use simulations for the Pacific Northwest, at 1-kilometer resolution, across 20 Monte Carlo realizations, can produce over 17,000 tabular and spatial outputs. A more robust management strategy is to store scenario-based, land-use datasets within a generalized...
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USGS scientists often face computationally intensive tasks that require high-throughput computing capabilities. Several USGS facilities use HTCondor to run their computational pools but are not necessarily connected to the larger USGS pool. This project demonstrated how to connect HTCondor pools by flocking, or coordinating, within the USGS. In addition to flocking the Upper Midwest Environmental Science Center and the Wisconsin Water Science Center, we have flocked with the USGS Advanced Research Computing Yeti supercomputing cluster and other water science centers. We also developed tutorials on how to sandbox code using Docker within the USGS environment for use with high-throughput computing. A main accomplishment...
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The purpose of this project was to support the enhanced search, access, and visualization capability for disaster maps and other contributed products on the public USGS Hazards Data Distribution System (HDDS) (U.S. Geological Survey, 2015). These products are often provided to USGS by collaborators for sharing across the response community during the course of an emergency event response; however, in the past, they were not easy for users to discover or access. This project involved the design, testing, and delivery of a new capability for HDDS to ingest, catalog, and display informational or value-added products when provided in a variety of formats. As a result of this work, the user community will be able to...
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Science is an increasingly collaborative endeavor. In an era of Web-enabled research, new tools reduce barriers to collaboration across traditional geographic and disciplinary divides and improve the quality and efficiency of science. Collaborative online code management has moved project collaboration from a manual process of email and thumb drives into a traceable, streamlined system where code can move directly from the command-line onto the Web for discussion, sharing, and open contributions. Within the USGS, however, data have no such analogous system. To bring data collaboration and sharing within the USGS to the next level, we are missing crucial components. The sbtools project team built sbtools, an R interface...
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We developed an Internet of Things (IoT) prototype and associated cloud infrastructure for camera-based data collection and initial processing of river streamflow using the cloud (fig. 1). This pilot successfully created a hardware and cloud infrastructure to collect and upload video from a camera gage at San Pedro Creek in San Antonio, Texas. Using a ThingLogix Foundry instance in the Amazon Webservices Cloud, we have created a cloud framework that can auto-provision new camera-based gaging equipment, as well as process incoming videos into image frames for the computation of streamflow. Additionally, we began testing of serving timeseries data from a camera gage (water level and CPU temperature) using real-time...
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U.S. Geological Survey (USGS) scientists are at the forefront of research that is critical for decision-making, particularly through the development of models (Bayesian networks, or BNs) that forecast coastal change. The utility of these tools outside the scientific community has been limited because they rely on expensive, technical software and a moderate understanding of statistical analyses. We proposed to convert one of our models from proprietary to freely available open-source software, resulting in a portable interactive web-interface. The resulting product will serve as a prototype to demonstrate how interdisciplinary USGS science and models can be transformed into an approachable format for decision-makers....
Wildfires are increasing across the western U.S., causing damage to ecosystems and communities. Addressing the fire problem requires understanding the trends and drivers of fire, yet most fire data is limited only to recent decades. Tree-ring fire scars provide fire records spanning 300-500 years, yet these data are largely inaccessible to potential users. Our project will deliver the newly compiled North American Fire Scar Network — 2,592 sites, 35,602 trees, and > 300,000 fire records — to fire scientists, managers and the public through an online application that will provide tools to explore, visualize, and analyze fire history data. The app will provide raw and derived data products, graphics, statistical summaries,...
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Computational models are important tools that aid process understanding, hypothesis testing, and data interpretation. The ability to easily couple models from various domains such as, surface-water and groundwater, to form integrated models will aid studies in water resources. This project investigates the use of the Community Surface Dynamics Modeling System (CSDMS) Modeling Framework (CMF) to couple existing USGS hydrologic models into integrated models. The CMF provides a Basic Model Interface (BMI), in a range of common computer languages, that enables model coupling. In addition, the CMF also provides a Python wrapper for any model that adopts the BMI. In this project the Precipitation-Runoff Modeling...
The sustainability of coastal water resources is being affected by climate change, sea level rise, and modifications to land use and hydrologic systems. To prepare for and respond to these drivers of hydrologic change, coastal water managers need real-time data, an understanding of temporal trends, and information about how current and historical data compare. Coastal water managers often must make decisions based on information pieced together from multiple sources because the available data and tools are scattered across various databases and websites; to aid coastal water managers, a website that consolidates data from multiple organizations and provides statistical analysis of hydrologic and water quality data...
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The USGS 3D Elevation Program (3DEP) is managing the acquisition of lidar data across the Nation for high resolution mapping of the land surface, useful for multiple applications. Lidar data is initially collected as 3-dimensional “point clouds” that map the interaction of the airborne laser with earth surface features, including vegetation, buildings, and ground features. Generally the product of interest has been high resolution digital elevation models generated by filtering the point cloud for laser returns that come from the ground surface and removing returns from vegetation, buildings, powerlines, and other above ground features. However, there is a wealth of information in the full point cloud on vegetation...
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Increasing attention is being paid to the importance of proper scientific data management and implementing processes that ensure that products being released are properly documented. USGS policies have been established to properly document not only publications, but also the related data and software. This relatively recent expansion of documentation requirements for data and software may present a daunting challenge for many USGS scientists whose major focus is their physical science and who have less expertise in information science. As a proof of concept, this project has created a software solution that facilitates this process through a user-friendly, but comprehensive, interface embedded in an existing...
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A BioBlitz is a field survey method for finding and documenting as many species as possible in a specific area over a short period. The National Park Service and National Geographic Society hosted the largest BioBlitz survey ever in 2016; people in more than 120 national parks used the iNaturalist app on mobile devices to document organisms they observed. Resulting records have Global Positioning System (GPS) coordinates, include biological accuracy assessments, and provide an unprecedented snapshot of biodiversity nationwide. Additional processing and analysis would make these data available to inform conservation and management decisions. This project developed a process to integrate iNaturalist data with existing...