Classification of digitized old maps

Talich, Milan; Böhm, Ondřej; Soukup, Lubomír

doi:10.1201/9780429505645-32

Cited by 2 publications

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“…The use of CORONA archive images and other similar datasets for producing historical land cover maps has already been presented in the literature [25][26][27][28]. In [29], the researchers used aerial photographs from 1944 to map agricultural lands and forests, while CORONA satellite images were used to map agricultural lands following an objectbased image analysis (OBIA).…”

Section: Introductionmentioning

confidence: 99%

Land Cover Mapping from Colorized CORONA Archived Greyscale Satellite Data and Feature Extraction Classification

Αγαπίου

2021

Land

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Land cover mapping is often performed via satellite or aerial multispectral/hyperspectral datasets. This paper explores new potentials for the characterisation of land cover from archive greyscale satellite sources by using classification analysis of colourised images. In particular, a CORONA satellite image over Larnaca city in Cyprus was used for this study. The DeOldify Deep learning method embedded in the MyHeritage platform was initially applied to colourise the CORONA image. The new image was then compared against the original greyscale image across various quality metric methods. Then, the geometric correction of the CORONA coloured image was performed using common ground control points taken for aerial images. Later a segmentation process of the image was completed, while segments were selected and characterised for training purposes during the classification process. The latest was performed using the support vector machine (SVM) classifier. Five main land cover classes were selected: land, water, salt lake, vegetation, and urban areas. The overall results of the classification process were then evaluated. The results were very promising (>85 classification accuracy, 0.91 kappa coefficient). The outcomes show that this method can be implemented in any archive greyscale satellite or aerial image to characterise preview landscapes. These results are improved compared to other methods, such as using texture filters.

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Section: Introductionmentioning

confidence: 99%

Land Cover Mapping from Colorized CORONA Archived Greyscale Satellite Data and Feature Extraction Classification

Αγαπίου

2021

Land

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Deep learning approaches for delineating wetlands on historical topographic maps

Vynikal,

Müllerová,

Pacina

2024

Transactions in GIS

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Historical topographic maps are an important source of a visual record of the landscape, showing geographical elements such as terrain, elevation, rivers and water bodies, roads, buildings, and land use and land cover (LULC). Historical maps are scanned to their digital representation, a raster image. To quantify different classes of LULC, it is necessary to transform scanned maps to their vector equivalent. Traditionally, this has been done either manually, or by using (semi)automatic methods of clustering/segmentation. With the advent of deep neural networks, new horizons opened for more effective and accurate processing. This article attempts to use different deep‐learning approaches to detect and segment wetlands on historical Topographic Maps 1: 10000 (TM10), created during the 50s and 60s. Due to the specific symbology of wetlands, their processing can be challenging. It deals with two distinct approaches in the deep learning world, semantic segmentation and object detection, represented by the U‐Net and Single‐Shot Detector (SSD) neural networks, respectively. The suitability, speed, and accuracy of the two approaches in neural networks are analyzed. The results are satisfactory, with the U‐Net F1 score reaching 75.7% and the SSD object detection approach presenting an unconventional alternative.

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Classification of digitized old maps

Cited by 2 publications

References 0 publications

Land Cover Mapping from Colorized CORONA Archived Greyscale Satellite Data and Feature Extraction Classification

Land Cover Mapping from Colorized CORONA Archived Greyscale Satellite Data and Feature Extraction Classification

Deep learning approaches for delineating wetlands on historical topographic maps

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