Information extraction from paper maps using object oriented analysis (OOA)

DISCLAIMER This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the Faculty. i ABSTRACT Historical topographic maps are distinct sources of spatial information for hind-cast studies. They are acclaimed to be one of the most reliable legacy archives representing and describing geographic features prior to aerial photography and the present day satellite imagery. However, two major challenges are encountered in extracting information from these sources. These challenges are conceptual and technical emanating from scanning artefacts, inherent map complexity and analogousness, although information extraction has been manually done through digitizing, pixel-based methods and visual map analysis, which are time consuming and tedious. Hence, there is urgent need to explore robust and reliable methods such as the object-oriented analysis (OOA) to efficiently develop new information extraction techniques for scanned topographic paper maps. Therefore, this research investigated and answered questions about conceptualization, development, implementation and transferability of an OOA-based information extraction method for complex papers maps and potential applications. This study demonstrates the OOA-based information extraction technique on a 1967 topographic map of Nigeria. The work was structured broadly into three major parts. The first part investigated underpinning theoretical concepts of saliency and semantics to conceptualize a generic OOA-based information extraction framework. The second part consequently, translated the conceptualized procedure to develop multi-step object-centered rules and implemented the developed algorithm on input maps to robustly and accurately extract tangible thematic information, despite typical complexities known with paper maps. Thirdly, the research further probed how far OOA rules-sets can be transferred to comparable data sets of same series with slightly changed imaging conditions to see how robust and reliable will the once developed rule-sets perform. Results show the suitability of saliency and semantic to conceptualize and develop an OOA-based information extraction formalism for complex paper maps. Similarly, the created OOA rule-sets robustly and reliably extracted thematic information that corresponds to targeted map objects with accuracies of 95% for the hydrographic layer, 97% and 92% for correctness and completeness of symbols respectively, 70% for texts and 55 % for contour lines. Interestingly, transferability of the once created rule-set proved realistic after testing it on different map section of same map series with slight modifications. Empirical observation of the …