Reproducibility and Replicability in Remote Sensing Workflows
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 12901
Special Issue Editor
Interests: remote sensing and GIS-assisted decision support for ecosystem services; geospatial unmanned aircraft systems (UAS); provenance, reproducibility and replicability (R&R) in GIScience; international geospatial capacity building
Special Issue Information
Dear Colleagues,
In 2019, the National Academies of Sciences, Engineering, and Medicine published a consensus study report on reproducibility and replicability (R&R), with important implications for computationally intensive sciences. For half a century, remote sensing workflows have leveraged cutting-edge computational innovation in the face of complex sensor, hardware, and software versioning combined with general challenges to replicability across spatiotemporal dimensions. As we navigate a global pandemic, unprecedented drivers of personal remote sensing now amplify prior geospatial R&R-related questions such as provenance, interoperability, multiuser access, privacy, ownership, scale of analysis, fitness for use, intellectual property, and regulatory control. These and other factors both complicate the role of remote sensing and invite unprecedented opportunity for innovation within a convergent GIScience paradigm.
Remote Sensing announces a Special Issue dedicated to R&R in remote sensing workflows. The Special Issue seeks to balance the need for practical R&R demonstrations with a careful analysis of the core drivers and constraints behind R&R in GIScience. While methodologies presented may narrowly address specific geospatial applications, authors should contextualize findings and interpretations in the interests of convergent stakeholders. Papers are invited that address the following (or related) topics:
- practical demonstration and comparative analysis of R&R in remote sensing workflows;
- R&R innovations in geospatial unmanned aircraft systems (UAS), artificial intelligence and deep learning, big data, or other remote sensing trends;
- innovations and interoperability in provenance-enabled remote sensing services, methodologies, or workflows and corresponding implications for convergent GIScience stakeholder interests;
- historical analysis of R&R in remote sensing workflows over the last 50 years, particularly as they relate to changing geopolitical, economic, industrial, computational, academic, and other drivers; or
- best practices to enable R&R in international geospatial capacity building.
Prof. Dr. Jason A. Tullis
Guest Editor
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Keywords
- reproducibility
- replicability
- remote sensing
- workflows
- provenance
- interoperability
- convergence
- capacity building
- unmanned aircraft systems (UAS)
- artificial intelligence
- deep learning
- geoprocessing
- image processing
- versioned source control
- ethics in GIScience
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