Cornell University remote sensing program

There are no author-identified significant results in this report

Turbulence, Inequality, and Cheap Steel

Iron and steel production grew dramatically in the U.S. when mass production technologies for steel were adopted in the 1860s. According to new measures presented in this study, earnings inequality rose within the iron and steel industries about 1870, perhaps because technological uncertainty led to gambles and turbulence. Firms made a variety of technological choices and began formal research and development. Professional associations and journals for mechanical engineers and chemists appeared. A national market replaced local markets for iron and steel. An industrial union replaced craft unions. As new ore sources and cheap water transportation were introduced, new plants along the Great Lakes outcompeted existing plants elsewhere. Because new iron and steel plants in the 1870s were larger than any U.S. plants had ever been, cost accounting appeared in the industry and grew in importance. Uncertainty explains the rise in inequality better than a skill bias account, according to which differences among individuals generate greater differences in wages. Analogous issues of inequality come up with respect to recent information technology.technological change, Bessemer steel, technological uncertainty, turbulence, inequality, innovation

Video Content Understanding Using Text

The rise of the social media and video streaming industry provided us a plethora of videos and their corresponding descriptive information in the form of concepts (words) and textual video captions. Due to the mass amount of available videos and the textual data, today is the best time ever to study the Computer Vision and Machine Learning problems related to videos and text. In this dissertation, we tackle multiple problems associated with the joint understanding of videos and text. We first address the task of multi-concept video retrieval, where the input is a set of words as concepts, and the output is a ranked list of full-length videos. This approach deals with multi-concept input and prolonged length of videos by incorporating multi-latent variables to tie the information within each shot (short clip of a full-video) and across shots. Secondly, we address the problem of video question answering, in which, the task is to answer a question, in the form of Fill-In-the-Blank (FIB), given a video. Answering a question is a task of retrieving a word from a dictionary (all possible words suitable for an answer) based on the input question and video. Following the FIB problem, we introduce a new problem, called Visual Text Correction (VTC), i.e., detecting and replacing an inaccurate word in the textual description of a video. We propose a deep network that can simultaneously detect an inaccuracy in a sentence while benefiting 1D-CNNs/LSTMs to encode short/long term dependencies, and fix it by replacing the inaccurate word(s). Finally, as the last part of the dissertation, we propose to tackle the problem of video generation using user input natural language sentences. Our proposed video generation method constructs two distributions out of the input text, corresponding to the first and last frames latent representations. We generate high-fidelity videos by interpolating latent representations and a sequence of CNN based up-pooling blocks

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Query-Time Data Integration

Today, data is collected in ever increasing scale and variety, opening up enormous potential for new insights and data-centric products. However, in many cases the volume and heterogeneity of new data sources precludes up-front integration using traditional ETL processes and data warehouses. In some cases, it is even unclear if and in what context the collected data will be utilized. Therefore, there is a need for agile methods that defer the effort of integration until the usage context is established. This thesis introduces Query-Time Data Integration as an alternative concept to traditional up-front integration. It aims at enabling users to issue ad-hoc queries on their own data as if all potential other data sources were already integrated, without declaring specific sources and mappings to use. Automated data search and integration methods are then coupled directly with query processing on the available data. The ambiguity and uncertainty introduced through fully automated retrieval and mapping methods is compensated by answering those queries with ranked lists of alternative results. Each result is then based on different data sources or query interpretations, allowing users to pick the result most suitable to their information need. To this end, this thesis makes three main contributions. Firstly, we introduce a novel method for Top-k Entity Augmentation, which is able to construct a top-k list of consistent integration results from a large corpus of heterogeneous data sources. It improves on the state-of-the-art by producing a set of individually consistent, but mutually diverse, set of alternative solutions, while minimizing the number of data sources used. Secondly, based on this novel augmentation method, we introduce the DrillBeyond system, which is able to process Open World SQL queries, i.e., queries referencing arbitrary attributes not defined in the queried database. The original database is then augmented at query time with Web data sources providing those attributes. Its hybrid augmentation/relational query processing enables the use of ad-hoc data search and integration in data analysis queries, and improves both performance and quality when compared to using separate systems for the two tasks. Finally, we studied the management of large-scale dataset corpora such as data lakes or Open Data platforms, which are used as data sources for our augmentation methods. We introduce Publish-time Data Integration as a new technique for data curation systems managing such corpora, which aims at improving the individual reusability of datasets without requiring up-front global integration. This is achieved by automatically generating metadata and format recommendations, allowing publishers to enhance their datasets with minimal effort. Collectively, these three contributions are the foundation of a Query-time Data Integration architecture, that enables ad-hoc data search and integration queries over large heterogeneous dataset collections

Colonial legacies: shaping African cities

Differential institutions imposed during colonial rule continue to affect the spatial structure and urban interactions in African cities. Based on a sample of 318 cities across 28 countries using satellite data on built cover over time, Anglophone origin cities sprawl compared to Francophone ones. Anglophone cities have less intense land use and more irregular layout in the older colonial portions of cities, and more leapfrog development at the extensive margin. Results are impervious to a border experiment, many robustness tests, measures of sprawl, and sub-samples. Why would colonial origins matter? The British operated under indirect rule and a dual mandate within cities, allowing colonial and native sections to develop without an overall plan and coordination. In contrast, integrated city planning and land allocation mechanisms were a feature of French colonial rule, which was inclined to direct rule. The results also have public policy relevance. From the Demographic and Health Survey, similar households, which are located in areas of the city with more leapfrog development, have poorer connections to piped water, electricity, and landlines, presumably because of higher costs of providing infrastructure with urban sprawl

The Kyle Mammoth Project: An Archaeological, Paleoecological and Taphonomic Analysis

In 1964 the remains of a Woolly Mammoth (Mammuthus primigenius) was unearthed near the small farming community of Kyle, Saskatchewan. The salvage excavation that was conducted by the Natural History Museum of Saskatchewan (now the Royal Saskatchewan Museum) uncovered roughly twenty percent of a single animal which was determined to have died of natural causes twelve thousand years ago. No further analysis was ever conducted on the remains until now. The combination of a radiocarbon date that was obtained in 1964 that concluded a time frame congruent with Clovis occupation in North America and known Clovis occupation within the area surrounding Kyle prompted a more thorough taphonomic analysis to be conducted on the remains. The objective for the analysis was to use the identification of postmortem taphonomic markers such as intentional bone breakage patterns and cutmarks as a proxy for human intervention with the Kyle mammoth. An additional antemortem analysis was included to account for a healed lesion that was discovered on a thoracic vertebra. The cause of the lesion, although not concluded, raises questions as to human association with this particular mammoth as well as a pathological aspect relating to a well-documented phenomenon that occurred in Eurasian Woolly Mammoths. The addition of an osteological analysis sheds light on the species, sex, and age at death of the animal and an archaeological and paleocological background supplements the notion of human and proboscidean interactions by shedding light on the environment surrounding the area of Kyle roughly 12,000 years ago and the possibility of the two species coexisting in southwestern Saskatchewan

Utilizing Remote Multispectral Scanner Data and Computer Analysis Techniques

This research was designed to study the ability of present automatic computer analysis techniques with the use of multispectral scanner data to differentiate land use categories represented in a complex urban scene and in a selected flightline. An airborne multispectral scanner was used to collect the visible and reflective infrared data. A small subdivision near Lafayette, Indiana was selected as the test site for the urban land use study. Multispectral scanner data were collected over the subdivision on May 1, 1970 from an altitude of 915 meters. The data were collected in twelve wavelength bands from 0.40 to 1.00 micrometers by the scanner. The results indicated that computer analysis of multispectral data can be very accurate in classifying and estimating the natural and man-made materials that characterize land uses in an urban scene. A 1.6 km. wide and 16 km. long flightline located in Sullivan County, Indiana, which represented most major land use categories, was selected for analysis. Multispectral scanner data were collected on three flights from an altitude of 1,500 meters. Energy in twelve wavelength bands from 0.46 to 11.70 micrometers was recorded by the scanner. A new, more objective approach to computer training was developed for analysis of the three dates of data. Emphasis was placed on the standardization of a procedure for analysis of data. The procedure offered faster and consistently good duplication of attained results. The results indicated an ability for automatic computer analysis-of remotely sensed multispectral scanner data to characterize and map land use categories within the test area. Additionally, results indicated an alteration of the data analysis procedure and land use classification scheme

Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months