Forest cover mask from historical topographic maps based on image processing

This study aimed to obtain accurate binary forest masks which might be directly used in analysis of land cover changes over large areas. A sequence of image processing operations was conceived, parameterized and tested using various topographic maps from mountain areas in Poland and Switzerland. First, the input maps were filtered and binarized by thresholding in Hue-Saturation-Value colour space. The second step consisted of a set of morphological image analysis procedures leading to final forest masks. The forest masks were then assessed and compared to manual forest boundary vectorization. The Polish topographical map published in the 1930s showed low accuracy which could be attributed to methods of cartographic presentation used and degradation of original colour prints. For maps published in the 1970s, the automated forest extraction performed very well, with accuracy exceeding 97%, comparable to accuracies of manual vectorization of the same maps performed by nontrained operators. With this method, we obtained a forest cover mask for the entire area of the Polish Carpathians, easily readable in any Geographic Information System software

Toponym Recognition in Scanned Color Topographic Maps

International audienceTopographic paper maps are a common support for geographical information. In the field of document analysis of this kind of support, this paper proposes an automatic approach to extract and recognize toponyms. We present a technique based on image segmentation and connected component processing. Different filtering stages ensure the consistency of plausible characters and strings. Detected text areas are used to feed an OCR software and the recognized words are analyzed and corrected. The main advantage of our technique is that no assumption is made about the character font, size or orientation. Experimental results obtained are encouraging in term of recognition efficiency

Cooperative Text and Line Art Extraction from a Topographic Map

The black layer is digitized from a USGS topographic map digitized at 1000 dpi. The connected components of this layer are analyzed and separated into line art, text, and icons in two passes. The paired street casings are converted to polylines by vectorization and associated with street labels from the character recognition phase. The accuracy of character recognition is shown to improve by taking account of the frequently occurring overlap of line art with street labels. The experiments show that complete vectorization of the black line-layer bitmap is the major remaining problem

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201

New tools for the classification and filtering of historical maps

6openInternationalBothHistorical maps constitute an essential information for investigating the ecological and landscape features of a region over time. The integration of heritage maps in GIS models requires their digitalization and classification. This paper presents a semi-automatic procedure for the digitalization of heritage maps and the successive filtering of undesirable features such as text, symbols and boundary lines. The digitalization step is carried out using Object-based Image Analysis (OBIA) in GRASS GIS and R, combining image segmentation and machine-learning classification. The filtering step is performed by two GRASS GIS modules developed during this study and made available as GRASS GIS add-ons. The first module evaluates the size of the filter window needed for the removal of text, symbols and lines; the second module replaces the values of pixels of the category to be removed with values of the surrounding pixels. The procedure has been tested on three maps with different characteristics, the “Historical Cadaster Map for the Province of Trento” (1859), the “Italian Kingdom Forest Map” (1926) and the “Map of the potential limit of the forest in Trentino” (1992), with an average classification accuracy of 97%. These results improve the performance of classification of heritage maps compared to more classical methods, making the proposed procedure that can be applied to heterogeneous sets of maps, a viable approachopenGobbi, Stefano; Ciolli, Marco; La Porta, Nicola; Rocchini, Duccio; Tattoni, Clara; Zatelli, PaoloGobbi, S.; Ciolli, M.; La Porta, N.; Rocchini, D.; Tattoni, C.; Zatelli, P

전근대 토지대장과 지적도의 대화형 분석을 위한 시각화 설계

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 서진욱.We propose an interactive visualization design tool, called JigsawMap, for analyzing and mapping historical textual cadasters. A cadaster is an official register that records land properties (e.g., location, ownership, value and size) for land valuation and taxation. Such mapping of old and new cadasters can help historians understand the social and economic background of changes in land uses or ownership. JigsawMap can effectively connect the past land survey results to modern cadastral maps. In order to accomplish the connection process, three steps are performed: (1) segmentation of cadastral map, (2) visualization of textual cadastre, (3) and mapping interaction. We conducted usability studies and long term case studies to evaluate JigsawMap, and received positive responses. We summarize the evaluation results and present design guidelines for participatory design projects with historians. Followed by our study on JigsawMap, we further investigated on each components of our tool for more scalable map connection. First, we designed a hybrid algorithm to semi-automatically segment land pieces on cadastral map. The original JigsawMap provides interface for user to segment land pieces and the experiment result shows that segmentation algorithm accurately extracts the regions. Next, we reconsidered the visual encoding and simplified it to make textual cadastre more scalable. Since the former visual encoding relies on traditional map legend, the visual encoding can be selected based on user expert level. Finally, we redesigned layout algorithm to generate a better initial layout. We used evolution algorithm to articulate ambiguity problem of textual cadastre and the result less suffered from overlapping problem. Overall, our visualization design tool will provide an accurate segmentation result, give the user an option to select visual encoding that suits on their expert level, and generate more readable initial layout which gives an overview of cadastre layout.Chapter 1 Introduction 1 1.1 Background & Motivation 1 1.2 Main Contribution 7 1.3 Organization of the Dissertation 8 Chapter 2 Related Work 11 2.1 Map Data Visualization 11 2.2 Graph Layout Algorithms 13 2.3 Collaborative Map Editing Service 14 2.4 Map Image Segmentation 15 2.5 Premodern Cadastral Maps 17 2.6 Assessing Measures for Cartogram 18 Chapter 3 Visualizing and Mapping Premodern Textual Cadasters to Cadastral Maps 20 3.1 Textual Cadastre 21 3.2 Cadastral Maps 24 3.3 Paper-based Mapping Process and Obstacles 24 3.4 Task Flow in JigsawMap 26 3.5 Design Rationale 32 3.6 Evaluation 34 3.7 Discussion 40 3.8 Design Guidelines When Working with Historians 42 Chapter 4 Accurate Segmentation of Land Regions in Historical Cadastral Maps 44 4.1 Segmentation Pipeline 45 4.2 Preprocessing 46 4.3 Removal of Grid Line 48 4.4 Removal of Characters 52 4.5 Reconstruction of Land Boundaries 53 4.6 Generation of Polygons 55 4.7 Experimental Result 56 4.8 Discussion 59 Chapter 5 Approximating Rectangular Cartogram from Premodern Textual Cadastre 62 5.1 Challenges of the Textual Cadastre Layout 62 5.2 Quality Measures for Assessing Rectangular Cartogram 64 5.3 Quality Measures for Assessing Textual Cadastre 65 5.4 Graph Layout Algorithm 66 5.5 Results 72 5.6 Discussion 73 Chapter 6 Design of Scalable Node Representation for a Large Textual Cadastre 78 6.1 Motivation 78 6.2 Visual Encoding in JigsawMa 80 6.3 Challenges of Current Visual Encoding 81 6.4 Compact Visual Encoding 83 6.5 Results 84 6.6 Discussion 86 Chapter 7 Conclusion 88 Bibliography 90 Abstract in Korean 101Docto

English for Geodesy and Land Management Students: tutorial.

English for Geodesy and Land Management Students is the manual for the students majoring in this specialty «Geodesy and Land Management» at higher education institutions and aimed at mastering the English language for specific purposes in this domain. The manual consists of 2 parts comprising the key theoretical issues students study at their special classes. The 1st part consists of 11 units. The 2nd part consists of 14 units. Each unit is designed in the way to provide students with the possibility to practice all language skills giving them flexibility in the field of future professional sphere. In the last part of the tutorial students can find texts for supplementary reading useful for efficient independent work

Geographic features recognition for heritage landscape mapping – Case study: The Banda Islands, Maluku, Indonesia

This study examines methods of geographic features recognition from historic maps using CNN and OBIA. These two methods are compared to reveal which one is most suitable to be applied to the historic maps dataset of the Banda Islands, Indonesia. The characteristics of cartographic images become the main challenge in this study. The geographic features are divided into buildings, coastline, and fortress. The results show that CNN is superior to OBIA in terms of statistical performance. Buildings and coastline give excellent results for CNN analysis, while fortress is harder to be interpreted by the model. On the other hand, OBIA reveals a very satisfying result is very depending on the maps’ scales. In the aspect of technical procedure, OBIA offers easier steps in pre-processing, in-process and post-processing/finalisation which can be an advantage for a wide range of users over CNN