The objective of interactive geographic maps is to provide geographic information to a large audience in a captivating and intuitive way. Storytelling helps to create exciting experiences and to explain complex or otherwise hidden relationships of geospatial data. Furthermore, interactive 3D applications offer a wide range of attractive elements for advanced visual story creation and offer the possibility to convey the same story in many different ways. In this paper, we discuss and analyze storytelling techniques in 3D geographic visualizations so that authors and developers working with geospatial data can use these techniques to conceptualize their visualization and interaction design. Finally, we outline two examples which apply the given concepts.

In this work, realistically drawn objects are identified on digital maps by convolutional neural networks. For the first two experiments, 6200 images were retrieved from Pinterest. While alternating image input options, two binary classifiers based on Xception and InceptionResNetV2 were trained to separate maps and pictorial maps. Results showed that the accuracy is 95–97% to distinguish maps from other images, whereas maps with pictorial objects are correctly classified at rates of 87–92%. For a third experiment, bounding boxes of 3200 sailing ships were annotated in historic maps from different digital libraries. Faster R-CNN and RetinaNet were compared to determine the box coordinates, while adjusting anchor scales and examining configurations for small objects. A resulting average precision of 32% was obtained for Faster R-CNN and of 36% for RetinaNet. Research outcomes are relevant for trawling map images on the Internet and for enhancing the advanced search of digital map catalogues.

Digitalization in schools requires a rethinking of teaching materials and methods in all subjects. This upheaval also concerns traditional print media, like school atlases used in geography classes. In this work, we examine the cartographic technological feasibility of extending a printed school atlas with digital content by augmented reality (AR). While previous research rather focused on topographic three-dimensional (3D) maps, our prototypical application for Android tablets complements map sheets of the Swiss World Atlas with thematically related data. We follow a natural marker approach using the AR engine Vuforia and the game engine Unity. We compare two workflows to insert geo-data, being correctly aligned with the map images, into the game engine. Next, the imported data are transformed into partly animated 3D visualizations, such as a dot distribution map, curved lines, pie chart billboards, stacked cuboids, extruded bars, and polygons. Additionally, we implemented legends, elements for temporal and thematic navigation, a screen capture function, and a touch-based feature query for the user interface. We evaluated our prototype in a usability experiment, which showed that secondary school students are as effective, interested, and sustainable with printed as with augmented maps when solving geographic tasks.

This study compares participants’ performance in extracting information from three-dimensional pie charts with individually extruded sectors in a single map frame against two-dimensional pie and bar charts in adjacent map frames. Specifically, we examine the response accuracy and response times of 181 adults who were asked to (1) identify the highest magnitude, (2) estimate a proportion, (3) perform both at the same time, and (4) solve a map-related task using the two chart setups. For each task, charts were shown on backgrounds with increasing visual complexity: a blank, a borders-only, and a choropleth map. Furthermore, we tested whether participants’ performance improved through additional practice with the two chart types. We did not observe any differences in participants’ aggregated response accuracy or response times between the tested three-dimensional and two-dimensional chart types for the (1) highest magnitude task and (2) proportion task. However, participants solved the (3) combination task with two-dimensional pie and bar charts on a blank background more accurately and were faster in fulfilling the (4) spatial task with three-dimensional pie charts. The first difference, however, leveled for participants who gained more practice and who accomplished the combination task on maps with higher visual complexity, whereas the second difference persisted even for more trained subjects.

In recent years, convolutional neural networks (CNNs) have been applied successfully to recognise persons, their body parts and pose keypoints in photos and videos. The transfer of these techniques to artificially created images is rather unexplored, though challenging since these images are drawn in different styles, body proportions, and levels of abstraction. In this work, we study these problems on the basis of pictorial maps where we identify included human figures with two consecutive CNNs: We first segment individual figures with Mask R-CNN, and then parse their body parts and estimate their poses simultaneously with four different UNet++ versions. We train the CNNs with a mixture of real persons and synthetic figures and compare the results with manually annotated test datasets consisting of pictorial figures. By varying the training datasets and the CNN configurations, we were able to improve the original Mask R-CNN model and we achieved moderately satisfying results with the UNet++ versions. The extracted figures may be used for animation and storytelling and may be relevant for the analysis of historic and contemporary maps.

Charts are key elements in thematic cartography to visualize multivariate data. While taxonomies and construction theories of 2D charts are well-elaborated, a concise framework for classifying and creating 3D charts is still missing. In this paper, we therefore propose a taxonomy for 3D charts which distinguishes between 3D point, line, surface, and solid charts. Special focus is put on the properties of solid charts. Based on these properties and with the aid of open-source software, we create six solid chart maps in a virtual globe which depict spatio-temporal data of the Atlas of Switzerland.