New Tools for the Classification and Filtering of Historical Maps

Gobbi, Stefano; Ciolli, Marco; Porta, Nicola La; Rocchini, Duccio; Tattoni, Clara; Zatelli, Paolo

doi:10.3390/ijgi8100455

Cited by 21 publications

(32 citation statements)

References 61 publications

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“…Object based image classification of historical maps have been proven to work well, when the historical maps have a good quality. This can be seen in previous work [9,15], where the input maps area relatively clear with distinct colors for each land cover type. Unfortunately, historic maps have mixed qualities often with physical damages.…”

Section: Remote Sensing Of Historical Maps Using Ecognitionsupporting

confidence: 61%

“…Long time scale land use and land cover change analyses can be performed with the use of historical maps, which have been widely done in Central Europe [6,7] (see, for example, France [8], Czech Republic [7], Belgium [2], Italy [9], and Germany [4,[10][11][12]). In Germany, analyses of land use and land cover change include historical information that dates back approximately 200 years, specifically in the region of Upper Franconia [4,12], Bavarian Forest [13], Swabian Jura [11], and, more recently, Leipzig [10].…”

Section: Introductionmentioning

confidence: 99%

“…However, the extraction of relevant information from historical maps requires that the different features are quantifiable. For this purpose, historical maps have to be georeferenced, and features have to be digitized and categorized in the corresponding land use and land cover classes [9].…”

Section: Introductionmentioning

confidence: 99%

“…Of these, pixel-based or object-based are two of the most common ones applied in the generation of thematic maps [3]. Specifically, historical maps classification has been done using several approaches: manual classification [10], vector GIS models (e.g., in ArcGIS) [4,13], object-based GRASS-GIS tools [9], pixel-based and shape classification using the PatchStat function from the SDM Tools R package [12], pixel-based classification using HistMapR R package [15], and final visual correction in several cases [9,16], among others.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

et al. 2020

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The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.

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Section: Remote Sensing Of Historical Maps Using Ecognitionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

et al. 2020

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“…Extracting the text from images of maps has long been a research topic (Pierrot Deseilligny et al, 1995, Pouderoux et al, 2007, Yao-Yi Chiang and Knoblock, 2010, Gobbi et al, 2019, and seems feasible with current deep learning techniques given the progresses made on other optical character recognition problems with such techniques (Lecun et al, 1998, Zhou et al, 2017.…”

Section: Discussionmentioning

confidence: 99%

Inferring the Scale and Content of a Map Using Deep Learning

Touya

Brisebard

Quinton

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).

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Application of texture-based features for text non-text classification in printed document images with novel feature selection algorithm

et al. 2021

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Text non-text separation is one of the most essential pre-processing steps for any optical character recognition (OCR) system. As an OCR engine can only process texts, the non-texts present in an input document image are required to be suppressed at the initial level. Therefore, to build a complete OCR system, an efficient text non-text separation module needs to be developed. To this end, we have proposed a texture-based feature descriptor followed by a novel feature selection technique for region-based text non-text classification. First, we have incorporated rotation invariant property with local ternary pattern to form a new texture-based feature descriptor, rotation invariant local ternary pattern (RILTP). Next, a novel feature selection technique is proposed which is a modified version of binary particle swarm optimization (BPSO). For the evaluation of the proposed text non-text classification method, we have initially constructed a database consisting of 690 images of text and non-text regions extracted from 70 pages of RDCL 2015 and 75 pages of RDCL 2017 page segmentation competitions databases. In this database, each class contains 345 data samples. The proposed texturebased feature descriptor has obtained an accuracy of 97.09% on this database. Whereas, after applying BPSO, the feature dimension is reduced by approximately 55% and at the same time, the accuracy reaches 97.5%. Furthermore, in this work, another database is also created from Media team document pages to validate the robustness of this method. The second database comprises 100 text and 100 non-text images. The method has achieved 96.28% accuracy when it is trained with the first database and tested with the second database. The comparative study reveals the robustness and strength of the proposed method as it outnumbers many state-of-the-art texture-based features. Besides, the proposed feature selection method is also compared with various standard feature selection methods, and it has been observed that the proposed one outperforms all those methods considered here for comparison.

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New Tools for the Classification and Filtering of Historical Maps

Cited by 21 publications

References 61 publications

Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

Inferring the Scale and Content of a Map Using Deep Learning

Application of texture-based features for text non-text classification in printed document images with novel feature selection algorithm

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