Automatic Label Placement of Area-Features Using Deep Learning

Abstract

Abstract. 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.

Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space

“…Ebinger and Goulette [19] proposed an algorithm that used parallel lines to cut the area features to get the approximate skeleton line inside the area features and then positioned the labels of area features along the obtained skeleton line. Li et al [20] applied the concept of deep learning for label placement of area features. Another situation is that the labels of area features which places outside the boundary of area features due to the lack of enough space inside the features.…”

A Hybrid Discrete Artificial Bee Colony Algorithm Based on Label Similarity for Solving Point-Feature Label Placement Problem

“…), line features (rivers, roads, etc.) [8], and area features [9] (continents, countries, oceans, etc.) [9].…”

“…[8], and area features [9] (continents, countries, oceans, etc.) [9]. Since all three types of problems can be abstracted as combinatorial optimization problems according to the label candidate model and the label quality evaluation function, the number of labels for point features is the largest.…”