AN EXPERIMENTAL DESIGN APPROACH ON GEOREFERENCING

Georeferencing is one of the most important stages of digitizing analogue maps. It isaffected by many factors such as; scales and resolutions of maps, the number ofcontrol points, etc. In this study, four of these factors were investigated using 2 4factorial design in two dimensional georeferencing of cadastral maps. Factorialdesign determines, whether the selected factors have main and/or interaction effectson a response variable or not. Map scale, resolution of raster map, the number ofcontrol points and the coordinate transformation method were selected asexperimental factors. Then, main effects and interactions between these factors wereinvestigated. The results were statistically analyzed using analysis of variance(ANOVA), and a regression model was suggested to consider the significant mainand interaction effects of factors. It was observed that the two dimensionalgeoreferencing of maps were affected by each of the selected experimental factorsand by the interaction between the map scale and coordinate transformation method

AN EXPERIMENTAL DESIGN APPROACH ON GEOREFERENCING

Georeferencing is one of the most important stages of digitizing analogue maps. It is affected by many factors such as; scales and resolutions of maps, the number of control points, etc. In this study, four of these factors were investigated using 24 factorial design in two dimensional georeferencing of cadastral maps. Factorial design determines, whether the selected factors have main and/or interaction effects on a response variable or not. Map scale, resolution of raster map, the number of control points and the coordinate transformation method were selected as experimental factors. Then, main effects and interactions between these factors were investigated. The results were statistically analyzed using analysis of variance (ANOVA), and a regression model was suggested to consider the significant main and interaction effects of factors. It was observed that the two dimensional georeferencing of maps were affected by each of the selected experimental factors and by the interaction between the map scale and coordinate transformation method

Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks

Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and the large data volume of digital map archives, which can hold thousands of digitized map sheets. Traditional map processing techniques typically rely on manually collected templates of the symbol of interest, and thus are not suitable for large-scale information extraction. In order to digitally preserve such large amounts of valuable retrospective geographic information, high levels of automation are required. Herein, we propose an automated machine-learning based framework to extract human settlement symbols, such as buildings and urban areas from historical topographic maps in the absence of training data, employing contemporary geospatial data as ancillary data to guide the collection of training samples. These samples are then used to train a convolutional neural network for semantic image segmentation, allowing for the extraction of human settlement patterns in an analysis-ready geospatial vector data format. We test our method on United States Geological Survey historical topographic maps published between 1893 and 1954. The results are promising, indicating high degrees of completeness in the extracted settlement features (i.e., recall of up to 0.96, F-measure of up to 0.79) and will guide the next steps to provide a fully automated operational approach for large-scale geographic feature extraction from a variety of historical map series. Moreover, the proposed framework provides a robust approach for the recognition of objects which are small in size, generalizable to many kinds of visual documents.</div

Conversion of Cadastral Survey Information into LandXML Files using Machine Learning

Although new cadastral surveys can readily be produced in the industry standard LandXML format, there is a vast amount of pre-existing information which is only stored as image files. Automating the back-capture of this information would improve a process which is labour intensive and prone to human error. This project proposes a workflow to automate this process, in relation to Victorian cadastral survey information. Specific algorithms and outcomes are examined using a simplified sample cadastral plan. The literature review reveals that similar documentation processes have been undertaken in other fields, such as music (Calvo-Zaragoza et al., 2018). In the cadastral context only true to scale cadastral maps have been digitised but not surveyors’ sketches or field records (Ignjatić et al., 2018) A simple plan was created containing a closed parcel and two instrument points for creation and testing of the workflow. An analysis of the tasks required to extract the information needed for the LandXML files was undertaken. A pipeline was designed to perform the data extraction in a machine learning environment, which has been dubbed Double Filter Capture. It consists of two main workflows that handle the graphical information and the text elements separately, by means of Computer Vision and Optical Character Recognition algorithms, respectively. An implementation of the actions in the pipeline was trialled and barriers encountered discussed. Several Machine Learning algorithms were used for the required tasks, such as line detection, corner detection, image rotation, text detection and text extraction. The project gives some idea of the possibilities and limitations that a larger scale automated back-capture would face, when dealing with records of significantly greater complexity. It also points the way to further research required to refine the extraction process outlined here, for example including elements omitted in this project, such as occupation and other auxiliary information and hand-written records. This project demonstrates automated accurate data extraction from an image file is possible, however an extensive investment would be required in the programming stage, given the complexity and inconsistencies of existing plans that require back-capture

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

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