Reliability in Constrained Gauss-Markov Models: An Analytical and Differential Approach with Applications in Photogrammetry

This report was prepared by Jackson Cothren, a graduate research associate in the Department of Civil and Environmental Engineering and Geodetic Science at the Ohio State University, under the supervision of Professor Burkhard Schaffrin.This report was also submitted to the Graduate School of the Ohio State University as a dissertation in partial fulfillment of the requirements for the Ph.D. degree.Reliability analysis explains the contribution of each observation in an estimation model to the overall redundancy of the model, taking into account the geometry of the network as well as the precision of the observations themselves. It is principally used to design networks resistant to outliers in the observations by making the outliers more detectible using standard statistical tests.It has been studied extensively, and principally, in Gauss- Markov models. We show how the same analysis may be extended to various constrained Gauss-Markov models and present preliminary work for its use in unconstrained Gauss-Helmert models. In particular, we analyze the prominent reliability matrix of the constrained model to separate the contribution of the constraints to the redundancy of the observations from the observations themselves. In addition, we make extensive use of matrix differential calculus to find the Jacobian of the reliability matrix with respect to the parameters that define the network through both the original design and constraint matrices. The resulting Jacobian matrix reveals the sensitivity of reliability matrix elements highlighting weak areas in the network where changes in observations may result in unreliable observations. We apply the analytical framework to photogrammetric networks in which exterior orientation parameters are directly observed by GPS/INS systems. Tie-point observations provide some redundancy and even a few collinear tie-point and tie-point distance constraints improve the reliability of these direct observations by as much as 33%. Using the same theory we compare networks in which tie-points are observed on multiple images (n-fold points) and tie-points are observed in photo pairs only (two-fold points). Apparently, the use of two-fold tiepoints does not significantly degrade the reliability of the direct exterior observation observations. Coplanarity constraints added to the common two-fold points do not add significantly to the reliability of the direct exterior orientation observations. The differential calculus results may also be used to provide a new measure of redundancy number stability in networks. We show that a typical photogrammetric network with n-fold tie-points was less stable with respect to at least some tie-point movement than an equivalent network with n-fold tie-points decomposed into many two-fold tie-points

Accelerating SIFT on Parallel Architectures

SIFT is a widely-used algorithm that extracts features from images; using it to extract information from hundreds of terabytes of aerial and satellite photographs requires parallelization in order to be feasible. We explore accelerating an existing serial SIFT implementation with OpenMP parallelization and GPU execution

Accelerating Image Feature Comparisons using CUDA on Commodity Hardware

Given multiple images of the same scene, image registration is the process of determining the correct transformation to bring the images into a common coordinate system—i.e., how the images fit together. Feature based registration applies a transformation function to the input images before performing the correlation step. The result of that transformation, also called feature extraction, is a list of significant points in the images, and the registration process will attempt to correlate these points, rather than directly comparing the input images

FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding

Although Domain Adaptation in Semantic Scene Segmentation has shown impressive improvement in recent years, the fairness concerns in the domain adaptation have yet to be well defined and addressed. In addition, fairness is one of the most critical aspects when deploying the segmentation models into human-related real-world applications, e.g., autonomous driving, as any unfair predictions could influence human safety. In this paper, we propose a novel Fairness Domain Adaptation (FREDOM) approach to semantic scene segmentation. In particular, from the proposed formulated fairness objective, a new adaptation framework will be introduced based on the fair treatment of class distributions. Moreover, to generally model the context of structural dependency, a new conditional structural constraint is introduced to impose the consistency of predicted segmentation. Thanks to the proposed Conditional Structure Network, the self-attention mechanism has sufficiently modeled the structural information of segmentation. Through the ablation studies, the proposed method has shown the performance improvement of the segmentation models and promoted fairness in the model predictions. The experimental results on the two standard benchmarks, i.e., SYNTHIA $\to$ Cityscapes and GTA5 $\to$ Cityscapes, have shown that our method achieved State-of-the-Art (SOTA) performance.Comment: Accepted to CVPR'2

FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding in Open World

Continual Learning in semantic scene segmentation aims to continually learn new unseen classes in dynamic environments while maintaining previously learned knowledge. Prior studies focused on modeling the catastrophic forgetting and background shift challenges in continual learning. However, fairness, another major challenge that causes unfair predictions leading to low performance among major and minor classes, still needs to be well addressed. In addition, prior methods have yet to model the unknown classes well, thus resulting in producing non-discriminative features among unknown classes. This paper presents a novel Fairness Learning via Contrastive Attention Approach to continual learning in semantic scene understanding. In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness. Then, we propose an attention-based visual grammar approach to effectively model the background shift problem and unknown classes, producing better feature representations for different unknown classes. Through our experiments, our proposed approach achieves State-of-the-Art (SOTA) performance on different continual learning settings of three standard benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC. It promotes the fairness of the continual semantic segmentation model

Integration of Water Resource Models with Fayetteville Shale Decision Support and Information System

Significant issues can arise with the timing, location, and volume of surface water withdrawals associated with hydraulic fracturing of gas shale reservoirs as impacted watersheds may be sensitive, especially in drought years, during low flow periods, or during periods of the year when activities such as irrigation place additional demands on the surface supply of water. Significant energy production and associated water withdrawals may have a cumulative impact to watersheds over the short-term. Hence, hydraulic fracturing based on water withdrawal could potentially create shifts in the timing and magnitude of low or high flow events or change the magnitude of river flow at daily, monthly, seasonal, or yearly time scales. These changes in flow regimes can result in dramatically altered river systems. Currently little is known about the impact of fracturing on stream flow behavior. Within this context the objective of this study is to assess the impact of the hydraulic fracturing on the water balance of the Fayetteville Shale play area and examine the potential impacts of hydraulic fracturing on river flow regime at subbasin scale. This project addressed that need with four unique but integrated research and development efforts: 1) Evaluate the predictive reliability of the Soil and Water Assessment Tool (SWAT) model based at a variety of scales (Task/Section 3.5). The Soil and Water Assessment Tool (SWAT) model was used to simulate the across-scale water balance and the respective impact of hydraulic fracturing. A second hypothetical scenario was designed to assess the current and future impacts of water withdrawals for hydraulic fracturing on the flow regime and on the environmental flow components (EFCs) of the river. The shifting of these components, which present critical elements to water supply and water quality, could influence the ecological dynamics of river systems. For this purpose, we combined the use of SWAT model and Richter et al.’s (1996) methodology to assess the shifting and alteration of the flow regime within the river and streams of the study area. 2) Evaluate the effect of measurable land use changes related to gas development (well-pad placement, access road completion, etc.) on surface water flow in the region (Task/Section 3.7). Results showed that since the upsurge in shale-gas related activities in the Fayetteville Shale Play (between 2006 and 2010), shale-gas related infrastructure in the region have increase by 78%. This change in land-cover in comparison with other land-cover classes such as forest, urban, pasture, agricultural and water indicates the highest rate of change in any land-cover category for the study period. A Soil and Water Assessment Tool (SWAT) flow model of the Little Red River watershed simulated from 2000 to 2009 showed a 10% increase in storm water runoff. A forecast scenario based on the assumption that 2010 land-cover does not see any significant change over the forecast period (2010 to 2020) also showed a 10% increase in storm water runoff. Further analyses showed that this change in the stream-flow regime for the forecast period is attributable to the increase in land-cover as introduced by the shale-gas infrastructure. 3) Upgrade the Fayetteville Shale Information System to include information on watershed status. (Tasks/Sections 2.1 and 2.2). This development occurred early in the project period, and technological improvements in web-map API’s have made it possible to further improve the map. The current sites (http://lingo.cast.uark.edu) is available but is currently being upgraded to a more modern interface and robust mapping engine using funds outside this project. 4) Incorporate the methodologies developed in Tasks/Sections 3.5 and 3.7 into a Spatial Decision Support System for use by regulatory agencies and producers in the play. The resulting system is available at http://fayshale.cast.uark.edu and is under review the Arkansas Natural Resources Commission

A Lockpick's Guide to dataARC: Designing Infrastructures and Building Communities to Enable Transdisciplinary Research

The North Atlantic Biocultural Organization (NABO) community initiated dataARC to develop digital research infrastructures to support their work on long-term human-ecodynamics in the North Atlantic. These infrastructures were designed to address the challenges of sharing research data, the connections between those data and high-level interpretations, and the interpretations themselves. In parallel, they were also designed to support the reuse of diverse data that underpin transdisciplinary synthesis research and to contextualise materials disseminated widely to the public more firmly in their evidence base. This article outlines the research infrastructure produced by the project and reflects on its design and development. We outline the core motivations for dataARC's work and introduce the tools, platforms and (meta)data products developed. We then undertake a critical review of the project's workflow. This review focuses on our understanding of the needs of stakeholder groups, the principles that guided the design of the infrastructure, and the extent to which these principles are successfully promoted in the current implementation. Drawing on this assessment, we consider how the infrastructure, in whole or in part, might be reused by other transdisciplinary research communities. Finally, we highlight key socio-technical gaps that may emerge as structural barriers to transdisciplinary, engaged, and open research if left unaddressed

Vampires in the village Žrnovo on the island of Korčula: following an archival document from the 18th century

Središnja tema rada usmjerena je na raščlambu spisa pohranjenog u Državnom arhivu u Mlecima (fond: Capi del Consiglio de’ Dieci: Lettere di Rettori e di altre cariche) koji se odnosi na događaj iz 1748. godine u korčulanskom selu Žrnovo, kada su mještani – vjerujući da su se pojavili vampiri – oskvrnuli nekoliko mjesnih grobova. U radu se podrobno iznose osnovni podaci iz spisa te rečeni događaj analizira u širem društvenom kontekstu i prate se lokalna vjerovanja.The main interest of this essay is the analysis of the document from the State Archive in Venice (file: Capi del Consiglio de’ Dieci: Lettere di Rettori e di altre cariche) which is connected with the episode from 1748 when the inhabitants of the village Žrnove on the island of Korčula in Croatia opened tombs on the local cemetery in the fear of the vampires treating. This essay try to show some social circumstances connected with this event as well as a local vernacular tradition concerning superstitions

Roundtable on Numerical and Spatial Data

Each Speaker will briefly discuss his research, the types of data he uses, and the data management challenges he faces. The audience will then have the opportunity to ask questions

SLIDES: Geospatial Decision Support for Shale Gas Site Development

Presenter: Malcolm Williamson, Center for Advanced Spatial Technologies, University of Arkansas 50 slide