All Funded Projects

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...

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...

Identifying the leading edge of a biological invasion can be difficult. Many management and research entities have biological samples or surveys that may unknowingly contain data on nonindigenous species. The new Nonindigenous Aquatic Species (NAS) Database automated online tool “SEINeD” (Screen and Evaluate Invasive and Non-native Data) will allow a user to search for these...

Natural resources managers are regularly required to make decisions regarding upcoming restoration treatments, often based on little more than business as usual practices. To assist in the decision-making process, we created a tool that predicts site-specific soil moisture and climate for the upcoming year, and provides guidance on whether common restoration activities (i.e. seeding...

Over the past 40 years the National Wildlife Health center has collected wildlife health information from around the U.S. and beyond, amassing the world’s largest repository of wildlife-disease surveillance data. This project identified, characterized, and documented NWHC’s locally stored wildlife health datasets, a critical first step to migrating them to new laboratory- and public...

Access to up-to-date geospatial data is critical when responding to natural hazards-related crises, such as volcanic eruptions. To address the need to reliably provide access to near real-time USGS datasets, we developed a process to allow data managers within the USGS Volcano Hazard Program to programmatically publish geospatial webservices to a cloud-based instance of GeoServer hosted...

Global climate models are a key source of climate information and produce large amounts of spatially explicit data for various physical parameters. However, these projections have substantial uncertainties associated with them, and the datasets themselves can be difficult to work with. The project team created the first version (cst 0.1.0) of the Climate Futures Toolbox, an open source...

The Community for Data Integration (CDI) Risk Map Project is developing modular tools and services to benefit a wide group of scientists and managers that deal with various aspects of risk research and planning. Risk is the potential that exposure to a hazard will lead to a negative consequence to an asset such as human or natural resources. This project builds upon a Department of the...

Lower technical and financial barriers have led to a proliferation of lidar point-cloud datasets acquired to support diverse USGS projects. The objective of this effort was to implement an open-source, cloud-based solution through USGS Cloud Hosting Solutions (CHS) that would address the needs of the growing USGS lidar community. We proposed to allow users to upload point-cloud datasets...

Ice jams are a major hazard. The project team worked with the US Army Corps of Engineers, National Weather Service, Silver Jackets, and USGS stakeholders to develop a mobile-friendly prototype of an Ice Jam Hazard website and reporting system. The prototype shows how ice jam conditions can be recorded nationwide. The public can view and download ice jam information. Historic ice jam...

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...

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...