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

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Local and regional desertification indicators in a global perspective: Seminar proceedings

This volume contains the proceedings of the International Seminar on Local and Regional Desertification Indicators in a Global Perspective held in Beijing, China, in May 2005. Aim of the seminar was to provide a precious opportunity to exchange information and experiences about the identification and use of desertification B&I among representatives of UNCCD Annexes, while contributing to strengthen linkages among them and exploring possible synergies. The seminar was organised in the framework of the AIDCCD project (Active Exchange of Experiences on Indicators and Development of Perspective in the Context of UNCCD), aiming at developing and co-ordinating exchange of experience across the world among institutions involved in the implementation of the UNCCD regional Annexes

関心生起の空間分析とモデリング : 地理空間情報技術を応用した景観資源評価の新たな方法論

This thesis provides new methods for visual resource assessment and modeling, an important research theme in micro-scale level tourist/recreational site planning and management. By acquiring and manipulating a combination of digital camera images and geospatial information tools such as geographic information systems, the proposed assessment method draws results from the analysis and modeling of visitors\u27 visual interest during their on-site experiences of geographic space. To acquire spatial data on visitors’ visual interest, two surveys are conducted. First, participants are requested to photograph positive scenes while walking using digital cameras embedded with GPS and electronic compass. Next, the participants complete questionnaires that help them to evaluate their visual experiences and categorize visual objects. Second, the height or width of the visual objects projected in the photographs taken by the participants is measured using a laser distance meter. Based on the data acquired from these surveys, spatial point and line data of visitors’ visual interest, named points of visual interest (PVI) and lines of visual interest (LVI), are extracted. Four types of applications for the analysis and modeling of visual interest are presented. Two applications attempt to analyze visual interest from location point data that visitors\u27 interest generate. The first application conducts an exploratory analysis of spot characteristics; several spatial clusters are extracted based on similarities in preference levels. The characteristics of the representative spots are statistically described using multiple indicators including the above clusters. The second application involves modeling and visualizing the sightseeing potential of locations. Embedded into the algorithm of density computation are mechanisms for removing bias and weighting preference level scores. The subsequent two applications focus mainly on spatial line data of visitors’ interest visual lines. The third application creates a visualization of the spatial intensity of visual lines. Three map representation techniques are presented: density estimation for line data, grid-based aggregation, and flow data representation. In addition, the advantages and disadvantages of each technique are described. The final application described is the construction of a prediction model for visual interest flows. Spatial interaction models are used to predict the level of total flow between locations through explanatory variables related to origin and destination potentials, landscape elements, and distance between locations. These models allow one to assess the entire target area, rather than being limited strictly to the scenes that visitors perceive. They also overcome the notable limitations of typical photographic surveys, and they clearly show new and useful techniques in visual resource management and modeling for specific sites. The spatial intensity of PVI, LVI, and geovisualization provide location-specific potential and attractiveness of scores for scenes and spaces.首都大学東京, 2014-03-25, 博士(都市科学), 甲第433号首都大学東

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p