4 research outputs found
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
Recommended from our members
Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications To Human Settlement Modelling
Due to advances in information and communication technology, new ways of acquisition, storage, and analysis of digital data have emerged. This constitutes new opportunities, but also imposes challenges for many scientific disciplines, including the geospatial sciences, where the availability, accessibility, and spatio-temporal granularity and coverage of environmental, geographic, and socioeconomic data is steadily increasing. Multi-source data measuring identical or related processes typically increase the reliability of knowledge derived but also lead to higher levels of discrepancies. In order to fully benefit from the value of such multi-source data, the contained information needs to be extracted effectively and efficiently, employing adequate data integration, mining, and analysis techniques. This work demonstrates how the integration of coherent multi-source geospatial data supports information extraction and analysis to generate new knowledge of both, the data itself and the underlying phenomenon, exemplified by the spatio-temporal distribution of human settlements. I present three applications in the field of human settlement modelling where data integration is a key component for knowledge acquisition. These three applications consist of i) a deep-learning based classification framework for fully automated extraction of built-up areas from historical maps in the spatial domain, ii) a machine-learning based time series classification framework for estimating changes in built-up areas in the temporal domain, based on multispectral remote sensing time series data, and iii) a novel framework for an in-depth accuracy assessment of model-generated data, exemplified by the Global Human Settlement Layer, for a detailed analysis of data uncertainty in the spatio-temporal domain, as well as across different scales and aggregation levels, attempting to quantify the fitness-for-use of such data.</p
Análisis de motivos decorativos de tejidos y revestimientos cerámicos en el entorno de la visión artificial. Aplicación a la reconstrucción de motivos históricos y al diseño
El objetivo de esta tesis es la contribución a la creación, e implementación en herramientas informáticas, de una metodologÃa aplicable para el análisis y edición de imágenes procedentes del campo de los diseños cerámicos y textiles, y por extensión, de todas aquellas imágenes que siguen un patrón repetitivo y que, por tanto, se ajustan a la TeorÃa de Grupos de SimetrÃa. Para ello, se ha definido una metodologÃa de análisis dividida en etapas, en la que se va aumentando gradualmente el nivel de la información manejada, desde los pÃxeles de la imagen inicial, pasando por los objetos (formas o unidades básicas perceptúales) y los motivos (agrupaciones de objetos realizadas con criterios perceptúales) hasta llegar a la estructura del patrón, es decir, las distintas transformaciones geométricas que relacionan los elementos (objetos y motivos) que lo forman. La información estructural obtenida es utilizada con fines diversos: la clasificación de las imágenes según el Grupo de SimetrÃa del Plano del patrón, la reconstrucción de las imágenes aprovechando el conocimiento de qué partes están relacionadas por la estructura, y por último, la edición de patrones, tanto a nivel de formas y motivos, como de estructura, permitiendo realizar cambios estructurales con facilidad, con lo que se generan familias de patrones a partir de uno analizado. Las herramientas desarrolladas han sido probadas con un amplio conjunto de imágenes de patrones de procedencias muy diversas, destacando el estudio de los alicatados de la Alhambra de Granada y del Alcázar de Sevilla, asà como de textiles y, ampliando los objetivos iniciales, a diversos elementos del entorno urbano.Albert Gil, FE. (2006). Análisis de motivos decorativos de tejidos y revestimientos cerámicos en el entorno de la visión artificial. Aplicación a la reconstrucción de motivos históricos y al diseño [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1936Palanci