2018

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...

USGS will soon transition to the international metadata standards known collectively as ISO 19115. The open-ended nature of ISO benefits with much greater flexibility and vocabulary to describe research products. However, that flexibility means few constraints that can guide authors and ensure standardized, robust documentation across the bureau. This project proposed that the USGS data...

Identifying, extracting, and mobilizing information from current and historical literature is a time-consuming part of organizing and collating synthetic data productions. This project explored the use of algorithm-based methods to identify and extract occurrence information from the GeoDeepDive (GDD) literature database to support upkeep of the Nonindigenous Aquatic Species (NAS) data...

Spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. This problem of data variability is common across USGS Mission Areas; it presents an obstacle to developing national-scale products and to identifying areas with relatively good/bad data coverage. We compiled available data of known landslides into a national-scale, searchable...

The Nonindigenous Aquatic Species (NAS) Database and Alert System (https://nas.er.usgs.gov/default.aspx) provides a framework for the rapid dissemination of new invasions as they are incorporated into the NAS Database. The system notifies registered users of new sightings of >1,330 non-native aquatic species as part of national-scale early detection and rapid response systems (EDRR), and...

Droughts are becoming more frequent and severe and this trend is expected to continue in the coming century. Drought effects on natural resources include reduced water availability for plants and humans, as well as increased insect, disease, and vegetation mortality. Land managers need more information regarding how water availability may change and how drought will affect their sites in...