Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification

We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology. We introduce a novel criteria for selecting meaningful and discriminative exemplar patches. We optimize this function during training using a greedy submodular function optimization framework that gives a near-optimal solution with bounded approximation error. We use these selected exemplars as a dictionary basis and propose a spatially-aware sparse coding method to match testing images for identification while maintaining global shape correspondence. To accelerate the coding process for fast matching, we introduce a relaxed form that uses spatially-aware soft-thresholding during coding. Finally, we carry out an experimental study that demonstrates the effectiveness and efficiency of our exemplar selection and classification mechanisms, achieving $86.13\%$ accuracy on a difficult fine-grained species classification task distinguishing three types of fossil spruce pollen.Comment: CVMI 201

HOW DO NOVICE AND EXPERT LEARNERS REPRESENT, UNDERSTAND, AND DISCUSS GEOLOGIC TIME?

This dissertation examined the representations novice and expert learners constructed for the geologic timescale. Learners engaged in a three-part activity. The purpose was to compare novice learners’ representations to those of expert learners. This provided insight into the similarities and differences between their strategies for event ordering, assigning values and scale to the geologic timescale model, as well as their language and practices to complete the model. With a qualitative approach to data analysis informed by an expert-novice theoretical framework grounded in phenomenography, learner responses comprised the data analyzed. These data highlighted learners’ metacognitive thoughts that might not otherwise be shared through lectures or laboratory activities. Learners’ responses were analyzed using a discourse framework that positioned learners as knowers. Novice and expert learners both excelled at ordering and discussing events before the Phanerozoic, but were challenged with events during the Phanerozoic. Novice learners had difficulty assigning values to events and establishing a scale for their models. Expert learners expressed difficulty with determining a scale because of the size of the model, yet eventually used anchor points and unitized the model to establish a scale. Despite challenges constructing their models, novice learners spoke confidently using claims and few hedging phrases indicating their confidence in statements made. Experts used more hedges than novices, however the hedging comments were made about more complex conceptions. Using both phenomenographic and discourse analysis approaches for analysis foregrounded learners’ discussions of how they perceived geologic time and their ways of knowing and doing. This research is intended to enhance the geoscience community’s understanding of the ways novice and expert learners think and discuss conceptions of geologic time, including the events and values of time, and the strategies used to determine accuracy of scale. This knowledge will provide a base from which to support geoscience curriculum development at the university level, specifically to design activities that will not only engage and express learners’ metacognitive scientific practices, but to encourage their construction of scientific identities and membership in the geoscience community

Program and Abstracts from the Celebration of Student Scholarship, 2019

Program and Abstracts from the Celebration of Student Scholarship on April 24, 2019

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Holistic biomimicry: a biologically inspired approach to environmentally benign engineering

Humanity's activities increasingly threaten Earth's richness of life, of which mankind is a part. As part of the response, the environmentally conscious attempt to engineer products, processes and systems that interact harmoniously with the living world. Current environmental design guidance draws upon a wealth of experiences with the products of engineering that damaged humanity's environment. Efforts to create such guidelines inductively attempt to tease right action from examination of past mistakes. Unfortunately, avoidance of past errors cannot guarantee environmentally sustainable designs in the future. One needs to examine and understand an example of an environmentally sustainable, complex, multi-scale system to engineer designs with similar characteristics. This dissertation benchmarks and evaluates the efficacy of guidance from one such environmentally sustainable system resting at humanity's doorstep - the biosphere. Taking a holistic view of biomimicry, emulation of and inspiration by life, this work extracts overarching principles of life from academic life science literature using a sociological technique known as constant comparative method. It translates these principles into bio-inspired sustainable engineering guidelines. During this process, it identifies physically rooted measures and metrics that link guidelines to engineering applications. Qualitative validation for principles and guidelines takes the form of review by biology experts and comparison with existing environmentally benign design and manufacturing guidelines. Three select bio-inspired guidelines at three different organizational scales of engineering interest are quantitatively validated. Physical experiments with self-cleaning surfaces quantify the potential environmental benefits generated by applying the first, sub-product scale guideline. An interpretation of a metabolically rooted guideline applied at the product / organism organizational scale is shown to correlate with existing environmental metrics and predict a sustainability threshold. Finally, design of a carpet recycling network illustrates the quantitative environmental benefits one reaps by applying the third, multi-facility scale bio-inspired sustainability guideline. Taken as a whole, this work contributes (1) a set of biologically inspired sustainability principles for engineering, (2) a translation of these principles into measures applicable to design, (3) examples demonstrating a new, holistic form of biomimicry and (4) a deductive, novel approach to environmentally benign engineering. Life, the collection of processes that tamed and maintained themselves on planet Earth's once hostile surface, long ago confronted and solved the fundamental problems facing all organisms. Through this work, it is hoped that humanity has taken one small step toward self-mastery, thus drawing closer to a solution to the latest problem facing all organisms.Ph.D.Committee Chair: Bert Bras; Committee Member: David Rosen; Committee Member: Dayna Baumeister; Committee Member: Janet Allen; Committee Member: Jeannette Yen; Committee Member: Matthew Realf

Machine learning applications for geoscience problems

Geoscientists have used machine learning for at least three decades and the applications spam many fields, from seismic processing and interpretation, to remote sensing classification, to analysis of well log data, among many others. More popular in some fields (e.g. seismic interpretation, remote sensing analysis) than others (e.g. paleontology), machine learning tools can leverage research in different areas of geoscience. Although machine learning is becoming more popular in different fields of geoscience, some concepts of more modern applications, convolutional neural networks in particular, are still vaguely understood by non-practitioners. I present some of the key concepts of machine learning with more details on the foundations of convolutional neural networks and some techniques that can help better understand convolutional neural networks behavior. I then present five case studies, mostly using convolutional neural networks and transfer learning. Transfer learning is a methodology that allow us to repurpose filters created by convolutional neural networks on a primary task to perform a secondary task. The five case studies start with a broader application of convolutional neural networks for different geoscience images, including thin-sections and core photographs. Then I present a how to perform core classification using convolutional neural networks. Next, how microfossils can be classified by the same methodology. I present a more detailed analysis of transfer learning using different remote sensing datasets. In the final case study, I show applications of supervised learning techniques to help forecast Megaelectron-Volt electrons inside Earth’s outer radiation belt. I conclude the dissertation with a summary and comments on the expectation of future research

New Approaches in Social, Environmental Management and Policy to Address SDGs

The book comprises a selection of papers addressing some of the most relevant challenges and opportunities for addressing SDGs from many different perspectives. Papers in this collection cover the most recent lines and approaches of research in addressing SDGs and are all novel propositions that deepen the analysis of environmental, social and governance strategies in the adaptation of the society to meet the 17 SDGs