Deep learning approaches for delineating wetlands on historical topographic maps
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13520%2F24%3A43898937" target="_blank" >RIV/44555601:13520/24:43898937 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21110/24:00376357
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/10.1111/tgis.13193?af=R" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/tgis.13193?af=R</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1111/tgis.13193" target="_blank" >10.1111/tgis.13193</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning approaches for delineating wetlands on historical topographic maps
Original language description
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 aresatisfactory, with the U-Net F1 score reaching 75.7% and the SSD object detection approach presenting an unconventional alternative
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20705 - Remote sensing
Result continuities
Project
<a href="/en/project/SS05010090" target="_blank" >SS05010090: Water in the landscape of Czech Switzerland</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Transaction in GIS
ISSN
1361-1682
e-ISSN
1467-9671
Volume of the periodical
2024
Issue of the periodical within the volume
28
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
1400-1411
UT code for WoS article
001240738200001
EID of the result in the Scopus database
2-s2.0-85195455583