An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

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

Augmented reality over maps

Dissertação de mestrado integrado em Engenharia InformáticaMaps and Geographic Information System (GIS) play a major role in modern society, particularly on tourism, navigation and personal guidance. However, providing geographical information of interest related to individual queries remains a strenuous task. The main constraints are (1) the several information scales available, (2) the large amount of information available on each scale, and (3) difficulty in directly infer a meaningful geographical context from text, pictures, or diagrams that are used by most user-aiding systems. To that extent, and to overcome the aforementioned difficulties, we develop a solution which allows the overlap of visual information over the maps being queried — a method commonly referred to as Augmented Reality (AR). With that in mind, the object of this dissertation is the research and implementation of a method for the delivery of visual cartographic information over physical (analogue) and digital two-dimensional (2D) maps utilizing AR. We review existing state-of-art solutions and outline their limitations across different use cases. Afterwards, we provide a generic modular solution for a multitude of real-life applications, to name a few: museums, fairs, expositions, and public street maps. During the development phase, we take into consideration the trade-off between speed and accuracy in order to develop an accurate and real-time solution. Finally, we demonstrate the feasibility of our methods with an application on a real use case based on a map of the city of Oporto, in Portugal.Mapas e Sistema de Informação Geográfica (GIS) desempenham um papel importante na sociedade, particularmente no turismo, navegação e orientação pessoal. No entanto, fornecer informações geográficas de interesse a consultas dos utilizadores é uma tarefa árdua. Os principais dificuldades são (1) as várias escalas de informações disponíveis, (2) a grande quantidade de informação disponível em cada escala e (3) dificuldade em inferir diretamente um contexto geográfico significativo a partir dos textos, figuras ou diagramas usados. Assim, e para superar as dificuldades mencionadas, desenvolvemos uma solução que permite a sobreposição de informações visuais sobre os mapas que estão a ser consultados - um método geralmente conhecido como Realidade Aumentada (AR). Neste sentido, o objetivo desta dissertação é a pesquisa e implementação de um método para a visualização de informações cartográficas sobre mapas 2D físicos (analógicos) e digitais utilizando AR. Em primeiro lugar, analisamos o estado da arte juntamente com as soluções existentes e também as suas limitações nas diversas utilizações possíveis. Posteriormente, fornecemos uma solução modular genérica para uma várias aplicações reais tais como: museus, feiras, exposições e mapas públicos de ruas. Durante a fase de desenvolvimento, tivemos em consideração o compromisso entre velocidade e precisão, a fim de desenvolver uma solução precisa que funciona em tempo real. Por fim, demonstramos a viabilidade de nossos métodos com uma aplicação num caso de uso real baseado num mapa da cidade do Porto (Portugal)

Susceptibility Modeling and Mission Flight Route Optimization in a Low Threat, Combat Environment

Movement and transportation systems are a primary topic in the study of humans and their relationship with the environment. Only a few modes of transportation allow for nearly full freedom of movement that is unconstrained by rigid nodes and networks. Individual human travel (walking, climbing, swimming, etc.) is one example while rotorcraft travel is another. Although other criteria constrain movement, independence from a network allows for a unique examination of human spatial decision-making and choice behavior. This research analyzes helicopter flight route planning in a low threat combat environment with respect to geography. The particular problem addressed, which ultimately concerns the quantitative representation and mapping of helicopter susceptibility in a low threat, combat environment, is assisted by a Geographic Information System (GIS). Prior susceptibility research on helicopters is combined with the spatial analytical functions of a GIS to cartographically model three dimensional flight corridors and routes across four separate areas. GIS optimized flight routing plans that minimize helicopter susceptibility (maximize capability to avoid threats) are then compared to the conventional routes produced by human flight route planners using existing techniques. Findings indicate that although the GIS routes reduce susceptibility costs, they concomitantly decrease route diversity. There was no significant evidence that experience, expertise, landscape familiarity, age, or the amount of time taken to plan had any effect on the spatial character of the routes. Several spatial similarities between conventionally planned routes and GIS optimized routes were revealed that expose potential perceptual limitations imposed by the conventional flight planning paradigm. Implementation of geospatial technology could help eliminate these restrictions

Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery

In today\u27s world of vast information availability users often confront large unorganized amounts of data with limited tools for managing them. Motion imagery datasets have become increasingly popular means for exposing and disseminating information. Commonly, moving objects are of primary interest in modeling such datasets. Users may require different levels of detail mainly for visualization and further processing purposes according to the application at hand. In this thesis we exploit the geometric attributes of objects for dataset summarization by using a series of image processing and neural network tools. In order to form data summaries we select representative time instances through the segmentation of an object\u27s spatio-temporal trajectory lines. High movement variation instances are selected through a new hybrid self-organizing map (SOM) technique to describe a single spatio-temporal trajectory. Multiple objects move in diverse yet classifiable patterns. In order to group corresponding trajectories we utilize an abstraction mechanism that investigates a vague moving relevance between the data in space and time. Thus, we introduce the spatio-temporal neighborhood unit as a variable generalization surface. By altering the unit\u27s dimensions, scaled generalization is accomplished. Common complications in tracking applications that include occlusion, noise, information gaps and unconnected segments of data sequences are addressed through the hybrid-SOM analysis. Nevertheless, entangled data sequences where no information on which data entry belongs to each corresponding trajectory are frequently evident. A multidimensional classification technique that combines geometric and backpropagation neural network implementation is used to distinguish between trajectory data. Further more, modeling and summarization of two-dimensional phenomena evolving in time brings forward the novel concept of spatio-temporal helixes as compact event representations. The phenomena models are comprised of SOM movement nodes (spines) and cardinality shape-change descriptors (prongs). While we focus on the analysis of MI datasets, the framework can be generalized to function with other types of spatio-temporal datasets. Multiple scale generalization is allowed in a dynamic significance-based scale rather than a constant one. The constructed summaries are not just a visualization product but they support further processing for metadata creation, indexing, and querying. Experimentation, comparisons and error estimations for each technique support the analyses discussed