Geology topics

Bryan B Campbell

Publications and source records attributed to Bryan B Campbell.

3 recordsLinked to original sources

USGS2026

Authoritative knowledge infrastructure for GeoAI: Implications for scientific spatial data providers

Geospatial Artificial Intelligence (GeoAI) increasingly relies on automated analysis of authoritative geospatial data, exposing limitations in data infrastructures originally designed for simple dissemination rather than for knowledge-driven use. From the perspective of scientific spatial data providers, the challenge is enabling GeoAI responsibly while retaining semantic clarity, provenance, reproducibility, and governance. This short paper (i) examines how GeoAI exposed limitations within existing geospatial data infrastructures, and (ii) identifies provider-side requirements for an authoritative knowledge infrastructure. Drawing on GIScience, scientific workflow, and operational AI literature, the paper outlines implications for national mapping organizations that seek to support advanced analytics without jeopardizing scientific rigor or stewardship responsibilities.

Conference Paper↗

USGS2023

Reimagining standardization and geospatial interoperability in today’s GeoAI culture

Integrating Geospatial Artificial Intelligence (GeoAI) into our technological landscape has revolutionized our capacity to understand and engage with the world. However, the burgeoning adoption of GeoAI applications has underscored the imperative of data, format, and conveyance standardization and enhancing geospatial interoperability. This vision paper delves into the intricacies of the evolving GeoAI environment, emphasizing the indispensable role of standardized practices and elevated interoperability. By synthesizing insights from geography, computer science, and data ethics, this contribution envisions a future characterized by seamless synergy between AI systems and geospatial data, driving impactful decision-making and transformative innovation.

Conference Paper↗

USGS2023

A guide to creating an effective big data management framework

Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey data without existing or outdated standards. This research aims to evaluate the components of data conveyance systems, such as transfer methods, tracking, and automation, to guide their improved performance. Specifically, organizations face the challenges of slow dispatch time and manual intervention when conveying data into, within, and from their systems. Conveyance often requires skilled workers when the system depends on physical media such as hard drives, particularly when terabyte transfers are required. In addition, incomplete or inconsistent metadata may necessitate manual intervention, process changes, or both. A proposed solution is organization-wide guidance for efficient data conveyance. That guidance involves systems analysis to outline a data management framework, which may include understanding the minimum requirements of data manifests, specification of transport mechanisms, and improving automation capabilities.

Journal of Big Data↗