Exploring Semi-Automatic Map Labeling
Label placement in maps is a very challenging task that is critical for the overall map quality. Most previous work focused on designing and implementing fully automatic solutions, but the resulting visual and aesthetic quality has not reached the same level of sophistication that skilled human cartographers achieve. We investigate a different strategy that combines the strengths of humans and algorithms. In our proposed method, first an initial labeling is computed that has many well-placed labels but is not claiming to be perfect. Instead it serves as a starting point for an expert user who can then interactively and locally modify the labeling where necessary. In an iterative human-in-the-loop process alternating between user modifications and local algorithmic updates and refinements the labeling can be tuned to the user's needs. We demonstrate our approach by performing different possible modification steps in a sample workflow with a prototypical interactive labeling editor. Further, we report computational performance results from a simulation experiment in QGIS, which investigates the differences between exact and heuristic algorithms for semi-automatic map labeling. To that end, we compare several alternatives for recomputing the labeling after local modifications and updates, as a major ingredient for an interactive labeling editor.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semi-automatic annotation of the UCU accents speech corpus
Annotation and labeling of speech tasks in large multitask speech corpora is a necessary part of preparing a corpus for distribution. We address three approaches to annotation and labeling: manual, semi automatic and aut…
Event DetectionSpeech RecognitionTracking Based Semi-Automatic Annotation for Scene Text Videos
Recently, video scene text detection has received increasing attention due to its comprehensive applications. However, the lack of annotated scene text video datasets has become one of the most important problems, which …
Scene Text Detectiontext annotationText DetectionReducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning
Labeling datasets is a noteworthy challenge in machine learning, both in terms of cost and time. This research, however, leverages an efficient answer. By exploring label propagation in semi-supervised learning, we can s…
Sentiment Analysistext-classificationText ClassificationWhen Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
Labeling a module defective or non-defective is an expensive task. Hence, there are often limits on how much-labeled data is available for training. Semi-supervised classifiers use far fewer labels for training models. H…
Open-Ended Question AnsweringSemi-Automatic Labeling for Deep Learning in Robotics
In this paper, we propose Augmented Reality Semi-automatic labeling (ARS), a semi-automatic method which leverages on moving a 2D camera by means of a robot, proving precise camera tracking, and an augmented reality pen …
Deep LearningObjectobject-detectionObject Detection