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1. Workshop Report: Rethinking NSF’s Computational Ecosystem for 21st Century Science and Engineering

   Reed, Daniel A. ; Lifka, David ; Swanson, David ; Amaro, Rommie ; Wilkins-Diehr, Nancy ( September 2018 , NSF Workshop Reports)

   Reed, Daniel A. ; Lifka, David ; Swanson, David ; Amaro, Rommie ; Wilkins-Diehr, Nancy (Ed.)

   This report summarizes the discussions from a workshop convened at NSF on May 30-31, 2018 in Alexandria, VA. The overarching objective of the workshop was to rethink the nature and composition of the NSF-supported computational ecosystem given changing application requirements and resources and technology landscapes. The workshop included roughly 50 participants, drawn from high-performance computing (HPC) centers, campus computing facilities, cloud service providers (academic and commercial), and distributed resource providers. Participants spanned both large research institutions and smaller universities. Organized by Daniel Reed (University of Utah, chair), David Lifka (Cornell University), David Swanson (University of Nebraska), Rommie Amaro (UCSD), and Nancy Wilkins-Diehr (UCSD/SDSC), the workshop was motivated by the following observations. First, there have been dramatic changes in the number and nature of applications using NSF-funded resources, as well as their resource needs. As a result, there are new demands on the type (e.g., data centric) and location (e.g., close to the data or the users) of the resources as well as new usage modes (e.g., on-demand and elastic). Second, there have been dramatic changes in the landscape of technologies, resources, and delivery mechanisms, spanning large scientific instruments, ubiquitous sensors, and cloud services, among others. 

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2. Developing an Introductory UAV/Drone Mapping Training Program for Seagrass Monitoring and Research

   https://doi.org/10.3390/drones4040070  

   Yang, Bo ; Hawthorne, Timothy L. ; Hessing-Lewis, Margot ; Duffy, Emmett J. ; Reshitnyk, Luba Y. ; Feinman, Michael ; Searson, Hunter ( December 2020 , Drones)

   null (Ed.)

   Unoccupied Aerial Vehicles (UAVs), or drone technologies, with their high spatial resolution, temporal flexibility, and ability to repeat photogrammetry, afford a significant advancement in other remote sensing approaches for coastal mapping, habitat monitoring, and environmental management. However, geographical drone mapping and in situ fieldwork often come with a steep learning curve requiring a background in drone operations, Geographic Information Systems (GIS), remote sensing and related analytical techniques. Such a learning curve can be an obstacle for field implementation for researchers, community organizations and citizen scientists wishing to include introductory drone operations into their work. In this study, we develop a comprehensive drone training program for research partners and community members to use cost-effective, consumer-quality drones to engage in introductory drone mapping of coastal seagrass monitoring sites along the west coast of North America. As a first step toward a longer-term Public Participation GIS process in the study area, the training program includes lessons for beginner drone users related to flying drones, autonomous route planning and mapping, field safety, GIS analysis, image correction and processing, and Federal Aviation Administration (FAA) certification and regulations. Training our research partners and students, who are in most cases novice users, is the first step in a larger process to increase participation in a broader project for seagrass monitoring in our case study. While our training program originated in the United States, we discuss our experiences for research partners and communities around the globe to become more confident in introductory drone operations for basic science. In particular, our work targets novice users without a strong background in geographic research or remote sensing. Such training provides technical guidance on the implementation of a drone mapping program for coastal research, and synthesizes our approaches to provide broad guidance for using drones in support of a developing Public Participation GIS process. 

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3. Effects of Neural Network Architecture on Topography Estimation From Satellite Imagery for Multi-Terrain Autonomous Vehicle Path Planning and Control

   https://doi.org/10.1109/MOST57249.2023.00021  

   Lynch, Ryan ; Beknalkar, Sumedh ; Lynch, Jack ; Mazzoleni, Andre ; Bryant, Matthew ( May 2023 , 2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST))

   Global warming is one of the world’s most pressing issues. The study of its effects on the polar ice caps and other arctic environments, however, can be hindered by the often dangerous and difficult to navigate terrain found there. Multi-terrain autonomous vehicles can assist researchers by providing a mobile platform on which to collect data in these harsh environments while avoiding any risk to human life and speeding up the research process. The mechanical design and ultimate efficacy of these autonomous robotic vehicles depends largely on the specific missions they are deployed for, but terrain conditions can vary wildly geographically as well as seasonally, making mission planning for these unmanned vehicles more difficult. This paper proposes the use of various UNet-based neural network architectures to generate digital elevation maps from satellite images, and explores and compares their efficacy on a single set of training and validation datasets generated from satellite imagery. These digital elevation maps generated by the model could be used by researchers not only to track the change in arctic topography over time, but to quickly provide autonomous exploratory research rovers with the topographical information necessary to decide on optimal paths during the mission. This paper analyzes different model architectures and training schemes: a traditional UNet, a traditional UNet with data augmentation, a UNet with a single active skip-layer vision transformer (ViT), and a UNet with multiple active skip-layer ViT. Each model was trained on a dataset of satellite images and corresponding digital elevation maps of Ellesmere Island, Canada. Utilizing ViTs did not demonstrate a significant improvement in UNet performance, though this could change with longer training. This paper proposes opportunities to improve performance for these neural networks, as well as next steps for further research, including improving the diversity of images in the dataset, generating a testing dataset from a completely different geographic location, and allowing the models more time to train. 

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4. Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets

   https://doi.org/10.1186/s12942-020-00201-9  

   Wang, Jue ; Kwan, Mei-Po ( December 2020 , International Journal of Health Geographics)

   null (Ed.)

   Abstract Background Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals’ geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. Methods This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. Results The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual’s home and all the other daily activity locations. Conclusions This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing. 

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5. Geospatial Technologies in Higher Education: Interactive Experiential Learning

   Ignatius, A. ( January 2019 , 2019 American Association of Geographers Annual Meeting)

   null (Ed.)

   Use of geospatial technology in higher education facilitates student engagement, promotes deeper understanding of material, and supports inquiry-based learning. However, technology must be applied strategically to generate optimal results. While use of web-based interactive modules and short video are constructive in curriculum, it is beneficial to combine this with exposure to hands-on, experimental, field-based technologies. Experiential learning with technology in the physical environment allows students to understand both the challenges and achievements of scientific investigation. This creates a more comprehensive understanding of science as an iterative process of experimentation and investigation and enrichens course material. This paper explores the uniquely advantageous opportunity Geography educators have to combine classroom-based technology with field-based educational experiences. Classroom use of Geographic Information Systems (GIS) and Remotely Sensed data is increasingly accessible with abundant free educational resources. In addition, field-based use of technology can promote location awareness and spatial critical thinking with the use of GPS-based activities. GPS-based educational units also highlight the growing field of citizen science and can be designed as service-based learning opportunities. Use of highly affordable micro unmanned aerial vehicles (UAV) demonstrates data collection procedures. In addition, exposure to Surveying techniques and the field of Geomatics highlights real-world applications of geographic technology. We discuss the use of geospatial technologies in introductory and advanced higher education courses and examine how technology can encourage access to scientific inquiry throughout the student population. 

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