Devin White

Sea-level rise and archaeological site destruction: An example from the southeastern United States using DINAA (Digital Index of North American Archaeology)

The impact of changing climate on terrestrial and underwater archaeological sites, historic buildings, and cultural landscapes can be examined through quantitatively-based analyses encompassing large data samples and broad geographic and temporal scales. The Digital Index of North American Archaeology (DINAA) is a multi-institutional collaboration that allows researchers online access to linked heritage data from multiple sources and data sets. The effects of sea-level rise and concomitant human population relocation is examined using a sample from nine states encompassing much of the Gulf and Atlantic coasts of the southeastern United States. A 1 m rise in sea-level will result in the loss of over >13,000 recorded historic and prehistoric archaeological sites, as well as over 1000 locations currently eligible for inclusion on the National Register of Historic Places (NRHP), encompassing archaeological sites, standing structures, and other cultural properties. These numbers increase substantially with each additional 1 m rise in sea level, with >32,000 archaeological sites and >2400 NRHP properties lost should a 5 m rise occur. Many more unrecorded archaeological and historic sites will also be lost as large areas of the landscape are flooded. The displacement of millions of people due to rising seas will cause additional impacts where these populations resettle. Sea level rise will thus result in the loss of much of the record of human habitation of the coastal margin in the Southeast within the next one to two centuries, and the numbers indicate the magnitude of the impact on the archaeological record globally. Construction of large linked data sets is essential to developing procedures for sampling, triage, and mitigation of these impacts.

LIDAR, Point Clouds, and Their Archaeological Applications

It is common in contemporary archaeological literature, in papers at archaeological conferences, and in grant proposals to see heritage professionals use the term LIDAR to refer to high spatial resolution digital elevation models and the technology used to produce them. The goal of this chapter is to break that association and introduce archaeologists to the world of point clouds, in which LIDAR is only one member of a larger family of techniques to obtain, visualize, and analyze three-dimensional measurements of archaeological features. After describing how point clouds are constructed, there is a brief discussion on the currently available software and analytical techniques designed to make sense of them.

Building a secure and feature-rich mobile mapping service app using HTML5: challenges and best practices

Managing a wide variety of mobile devices across multiple mobile operating systems is a security challenge for any organization [1, 2]. With the wide adoption of mobile devices to access work-related apps, there is an increase in third-party apps that might either misuse or improperly handle users personal or sensitive data [3]. HTML5 has been receiving wide attention for developing cross-platform mobile apps. According to International Data Corporation (IDC), by 2015, 80% of all mobile apps will be based in part or wholly upon HTML5 [4]. Though HTML5 provides a rich set of features for building an app, it is a challenge for organizations to deploy and manage HTML5 apps on wide variety of devices while keeping security policies intact. In this paper, we will describe an upcoming secure mobile environment for HTML5 apps, called Sencha Space that addresses these issues and discuss how it will be used to design and build a secure and cross-platform mobile mapping service app. We will also describe how HTML5 and a new set of related technologies such as Geolocation API, WebGL, Open Layers 3, and Local Storage, can be used to provide a high end and high performance experience for users of the mapping service app.

From Everywhere to Everywhere (FETE): Adaptation of a Pedestrian Movement Network Model to a Hybrid Parallel Environment

Shortest-path algorithms are hard to parallelize because they require a large number of global operations to estimate the costs of alternative routes. However, some geographic problems, such as locating archaeological sites and tracking the spread of infectious diseases, demand the ability to find a large number of the shortest paths on very large graphs or grids. Here, we present an approach based on the out-of-RAM Dijkstra shortest-path algorithm that can be employed in hybrid massively parallel or cloud environments. In this approach, we partition the graph, precompute all paths inside each partition, and then assemble the routes from precomputed paths. We demonstrate the utility of this approach by estimating travel frequency in pedestrian networks.

MIRAGE: A Framework for Data-Driven Collaborative High-Resolution Simulation

Information about how human populations shift in response to various stimuli is limited because no single model is capable of addressing these stimuli simultaneously, and integration of the best existing models has been challenging because of the vast disparity among constituent model purposes, architectures, scales, and execution environments. To demonstrate a potential model coupling for approaching this problem, three major model components are integrated into a fully coupled system that executes a worldwide infection-infected routine where a human population requires a food source for sustenance and an infected population can spread an infection when it is in contact with the remaining healthy population. To enable high-resolution data-driven model federation and an ability to capture dynamics and behaviors of billions of humans, a high-performance computing agent-based framework has been created and is demonstrated in this chapter.

A Fully Automated High-Performance Image Registration Workflow to Support Precision Geolocation for Imagery Collected by Airborne and Spaceborne Sensors

Deriving precise coordinates from airborne and spaceborne imagery, with uncertainty estimates, is very challenging. Doing so is significantly more difficult when imagery is coming from one or more sensors that have questionable and/or incomplete photogrammetric metadata. Before precision geolocation activities can take place, that metadata must be complete and consistent such that images are correctly registered to one another and the Earth’s surface. This chapter describes an automated, high-performance image registration workflow that is being built at Oak Ridge National Laboratory to meet this need and focuses on the core concepts and software libraries underlying its creation. Highly encouraging initial system performance metrics are included as well.

Optimizing Convolutional Neural Networks for Cloud Detection

Deep convolutional neural networks (CNNs) have become extremely popular and successful at a number of machine learning tasks. One of the great challenges of successfully deploying a CNN is designing the network: specifying the network topology (sequence of layer types) and configuring the network (setting all the internal layer hyper-parameters). There are a number of techniques which are commonly used to design the network. One of the most successful is a simple (but lengthy) random search. In this paper we demonstrate how a random search can be dramatically improved by a two-phase search. The first phase is a traditional random search on n network configurations. The second phase exploits a support vector machine to guide a second random search on N network configurations. We apply this technique to a dataset containing satellite imagery and demonstrate that we can, with very high accuracy, identify regions containing clouds which obscure the landscape below.

Sensor-agnostic photogrammetric image registration with applications to population modeling

While wide area motion imagery provides short-timescale temporal information, e.g., individual vehicle tracking, it lacks broader contextual information on the ambient distribution of populations within that area. We present a fusion approach to augment Iris video with broader-scale population data. Spectral, geometric, and geospatial limitations of the Iris video preclude the use of Iris video directly; this is overcome by photogrammetric registration of robust Deimos-2 imagery and ancillary processed products using a high performance sensoragnostic, multi-temporal registration workflow. We assess the accuracy and precision of the proposed workflow (~15 m; Euclidean) and demonstrate the potential to leverage the fusion of these data towards rapid, global-scale population distribution modeling. This has important implications to effective response to emergencies, especially in urban environments, where population density is driven largely by building heights, and a complementary, multi-scale understanding of the distribution and dynamics of people within that geographic area is required.

BioenergyKDF: enabling spatiotemporal data synthesis and research collaboration

The Bioenergy Knowledge Discovery Framework (BioenergyKDF) is a scalable, web-based collaborative environment for scientists working on bioenergy related research in which the connections between data, literature, and models can be explored and more clearly understood. The fully-operational and deployed system, built on multiple open source libraries and architectures, stores contributions from the community of practice and makes them easy to find, but that is just its base functionality. The BioenergyKDF provides a national spatiotemporal decision support capability that enables data sharing, analysis, modeling, and visualization as well as fosters the development and management of the U.S. bioenergy infrastructure, which is an essential component of the national energy infrastructure. The BioenergyKDF is built on a flexible, customizable platform that can be extended to support the requirements of any user community---especially those that work with spatiotemporal data. While there are several community data-sharing software platforms available, some developed and distributed by national governments, none of them have the full suite of capabilities available in BioenergyKDF. For example, this component-based platform and database independent architecture allows it to be quickly deployed to existing infrastructure and to connect to existing data repositories (spatial or otherwise). As new data, analysis, and features are added; the BioenergyKDF will help lead research and support decisions concerning bioenergy into the future, but will also enable the development and growth of additional communities of practice both inside and outside of the Department of Energy. These communities will be able to leverage the substantial investment the agency has made in the KDF platform to quickly stand up systems that are customized to their data and research needs.

Understanding climate-induced migration through computational modeling: A critical overview with guidance for future efforts

Climate change has the potential to displace large populations in many parts of the developed and developing world. Understanding why, how, and when environmental migrants decide to move is critical to successful strategic planning within organizations tasked with helping the affected groups, and mitigating their systemic impacts. One way to support planning is through the employment of computational modeling techniques. Models can provide a window into possible futures, allowing planners and decision makers to test different scenarios in order to understand what might happen. While modeling is a powerful tool, it presents both opportunities and challenges. This paper builds a foundation for the broader community of model consumers and developers by: providing an overview of pertinent climate-induced migration research, describing some different types of models and how to select the most relevant one(s), highlighting three perspectives on obtaining data to use in said model(s), and the consequences associated with each. It concludes with two case studies based on recent research that illustrate what can happen when ambitious modeling efforts are undertaken without sufficient planning, oversight, and interdisciplinary collaboration. We hope that the broader community can learn from our experiences and apply this knowledge to their own modeling research efforts.