AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING
Label placement is one of the most essential tasks in the fields of cartography and geographic information systems. Numerous studies have been conducted on the automatic label placement for the past few decades. In this study, we focus on automatic label placement of area-feature, which has been rel...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-08-01
|
Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2020/117/2020/isprs-archives-XLIII-B4-2020-117-2020.pdf |
id |
doaj-6b2266815c124d5f8b8735acba1ed174 |
---|---|
record_format |
Article |
spelling |
doaj-6b2266815c124d5f8b8735acba1ed1742020-11-25T03:48:13ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-08-01XLIII-B4-202011712210.5194/isprs-archives-XLIII-B4-2020-117-2020AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNINGY. Li0M. Sakamoto1T. Shinohara2T. Satoh3PASCO CORPORATION, 4-9-6 Aobadai, Meguro-ku, Tokyo 153-0042, JapanPASCO CORPORATION, 4-9-6 Aobadai, Meguro-ku, Tokyo 153-0042, JapanPASCO CORPORATION, 4-9-6 Aobadai, Meguro-ku, Tokyo 153-0042, JapanPASCO CORPORATION, 4-9-6 Aobadai, Meguro-ku, Tokyo 153-0042, JapanLabel placement is one of the most essential tasks in the fields of cartography and geographic information systems. Numerous studies have been conducted on the automatic label placement for the past few decades. In this study, we focus on automatic label placement of area-feature, which has been relatively less studied than that of point-feature and line-feature. Most of the existing approaches have adopted a rule-based algorithm, and there are limitations in expressing the characteristics of label placement for area-features of various shapes utilizing handcrafted rules, criteria, objective functions, etc. Hence, we propose a novel approach for automatic label placement of area-feature based on deep learning. The aim of the proposed approach is to obtain the complex and implicit characteristics of area-feature label placement by manual operation directly and automatically from training data. First, the area-features with vector format are converted into a binary image. Then a key-point detection model, which simultaneously detect and localize specific key-points from an image, is applied to the binary image to estimate the candidate positions of labels. Finally, the final label placement positions for each area-feature are determined via simple post-process. To evaluate the proposed approach, the experiments with cadastral data were conducted. The experimental results show that the ratios of the estimation errors within 1.2 m (corresponding to one pixel of the input image) were 92.6% and 94.5% in the center and upper-left placement style, respectively. It implies that the proposed approach could place the labels for area-features automatically and accurately.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2020/117/2020/isprs-archives-XLIII-B4-2020-117-2020.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Y. Li M. Sakamoto T. Shinohara T. Satoh |
spellingShingle |
Y. Li M. Sakamoto T. Shinohara T. Satoh AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
Y. Li M. Sakamoto T. Shinohara T. Satoh |
author_sort |
Y. Li |
title |
AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING |
title_short |
AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING |
title_full |
AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING |
title_fullStr |
AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING |
title_full_unstemmed |
AUTOMATIC LABEL PLACEMENT OF AREA-FEATURES USING DEEP LEARNING |
title_sort |
automatic label placement of area-features using deep learning |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2020-08-01 |
description |
Label placement is one of the most essential tasks in the fields of cartography and geographic information systems. Numerous studies have been conducted on the automatic label placement for the past few decades. In this study, we focus on automatic label placement of area-feature, which has been relatively less studied than that of point-feature and line-feature. Most of the existing approaches have adopted a rule-based algorithm, and there are limitations in expressing the characteristics of label placement for area-features of various shapes utilizing handcrafted rules, criteria, objective functions, etc. Hence, we propose a novel approach for automatic label placement of area-feature based on deep learning. The aim of the proposed approach is to obtain the complex and implicit characteristics of area-feature label placement by manual operation directly and automatically from training data. First, the area-features with vector format are converted into a binary image. Then a key-point detection model, which simultaneously detect and localize specific key-points from an image, is applied to the binary image to estimate the candidate positions of labels. Finally, the final label placement positions for each area-feature are determined via simple post-process. To evaluate the proposed approach, the experiments with cadastral data were conducted. The experimental results show that the ratios of the estimation errors within 1.2 m (corresponding to one pixel of the input image) were 92.6% and 94.5% in the center and upper-left placement style, respectively. It implies that the proposed approach could place the labels for area-features automatically and accurately. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2020/117/2020/isprs-archives-XLIII-B4-2020-117-2020.pdf |
work_keys_str_mv |
AT yli automaticlabelplacementofareafeaturesusingdeeplearning AT msakamoto automaticlabelplacementofareafeaturesusingdeeplearning AT tshinohara automaticlabelplacementofareafeaturesusingdeeplearning AT tsatoh automaticlabelplacementofareafeaturesusingdeeplearning |
_version_ |
1724499444437614592 |