K-span: Open and reproducible spatial analytics using scientific workflows

This paper describes the design, development, and testing of a general-purpose scientific-workflows tool for spatial analytics. Spatial analytics processes are frequently complex, both conceptually and computationally. Adaptation, documention, and reproduction of bespoke spatial analytics procedures represents a growing challenge today, particularly in this era of big spatial data. Scientific workflow systems hold the promise of increased openness and transparency with improved automation of spatial analytics processes. In this work, we built and implemented a KNIME spatial analytics (“K-span”) software tool, an extension to the general-purpose open-source KNIME scientific workflow platform. The tool augments KNIME with new spatial analytics nodes by linking to and integrating a range of existing open-source spatial software and libraries. The implementation of the K-span system is demonstrated and evaluated with a case study associated with the original process of construction of the Australian national DEM (Digital Elevation Model) in the Greater Brisbane area of Queensland, Australia by Geoscience Australia (GA). The outcomes of translating example spatial analytics process into a an open, transparent, documented, automated, and reproducible scientific workflow highlights the benefits of using our system and our general approach. These benefits may help in increasing users’ assurance and confidence in spatial data products and in understanding of the provenance of foundational spatial data sets across diverse uses and user groups

Geospatial User Feedback: how to raise users’ voice and collectively build knowledge at the same time

Geospatial data is used not only to contemplate reality but also, in combination with analytical tools, to generate new information that requires interpretation. In this process data users gain knowledge about the data and its limitations (the user side of data quality) as well as knowledge on the status and evolutions of the studied phenomena. Knowledge can be annotations on top of the data, responses to questions, a careful description of the processes applied, a piece of software code or scripts applied to the data, usage reports or a complete scientific paper. This paper proposes an extension of the current Open Geospatial Consortium standard for Geospatial User Feedback to include the required knowledge elements, and a practical implementation. The system can incrementally collect, store, and communicate knowledge elements created by users of the data and keep them linked to the original data by means of permanent data identifiers. The system implements a Web API to manage feedback items as a frontend to a database. The paper demonstrates how a JavaScript widget accessing this API as a client can be easily integrated into existing data catalogues, such as the ECOPotential web service or the GEOEssential data catalogue, to collectively collect and share knowledge

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach