While ordinary touchscreen-based interfaces on urban explorer applications draw much of a user's attention onto the screen, visual and audio-haptic augmented reality interfaces have emerged as the two main streams for enabling direct focus on the surroundings. However, neither interface alone satisfies users in the highly dynamic urban environment. This research investigates how the two complementary augmentation can coexist on one system and how people adapt to the situation by selecting the more suitable interface. A prototype was deployed in a field experiment in which participants explored points of interest in an urban environment with both interfaces. The engagement with the surroundings was compared with a touchscreen-based application. Most participants spontaneously switched between the two interfaces, which manifests the value of the availability of both interfaces on one system. The results point at the situated advantages of either interface and reveal the users' preferences when both interfaces are available.

This paper describes the development and evaluation of Undulating Covers (UnCovers), mobile interfaces that can change their surface texture to transmit information. The Pin Array UnCover incorporates sinusoidal ridges controlled by servomotors, which can change their amplitude and granularity. The Mylar UnCover is a more organic interface that exploits the buckling properties of Mylar, using muscle wires, to change the texture granularity. The prototype development process and evaluations show that very low frequency texture changes, using amplitude or granularity, can be distinguished with high levels of accuracy. Since small changes are perceptible, it is possible to incorporate such interfaces into mobile devices without drastically increasing their size or actuation requirements. Finally, ratings from participants indicate that UnCovers would be appropriate for attention-grabbing, or caring and supportive interpersonal messages.

Smart glasses suffer from obtrusive or cumbersome interaction techniques. Studies show that people are not willing to publicly use, for example, voice control or mid-air gestures in front of the face. Some techniques also hamper the high degree of freedom of the glasses. In this paper, we derive design principles for socially acceptable, yet versatile, interaction techniques for smart glasses based on a survey of related work. We propose an exemplary design, based on a haptic glove integrated with smart glasses, as an embodiment of the design principles. The design is further refined into three interaction scenarios: text entry, scrolling, and point-and-select. Through a user study conducted in a public space we show that the interaction technique is considered unobtrusive and socially acceptable. Furthermore, the performance of the technique in text entry is comparable to state-of-the-art techniques. We conclude by reflecting on the advantages of the proposed design.

Locating points of interest (POIs) in cities is typically facilitated by visual aids such as paper maps, brochures, and mobile applications. However, these techniques require visual attention, which ideally should be on the surroundings. Non-visual techniques for navigating towards specific POIs typically lack support for free exploration of the city or more detailed guidance. To overcome these issues, we propose a multimodal, wearable system for alerting the user of nearby recommended POIs. The system, built around a tactile glove, provides audio-tactile cues when a new POI is in the vicinity, and more detailed information and guidance if the user expresses interest in this POI. We evaluated the system in a field study, comparing it to a visual baseline application. The encouraging results show that the glove-based system helps keep the attention on the surroundings and that its performance is on the same level as that of the baseline.

The objective of this study is fast and accurate to detect the landslides automatically from shadow areas and non- shadow areas that use ADS-40 airborne multispectral image by stratified classification method. First, the shadow area was detected by the brightness method. The shadow and non shadow images were calculated Normalized Difference Vegetation Index (NDVI), and we used iterative self-organizing data analysis technique (ISODATA) unsupervised classification to classify the area of vegetation and non-vegetation. The highest overall classification accuracy of shaded and non-shaded Landslides was 85.75% and 92.75%, respectively. The classification of shaded area by 12-bit image radiation information has a certain capacity. This automated process can be effectively and quickly obtain information of Landslide.