The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution of land-use and land-cover (LULC) mapping. This approach taps into the collective power of the public to share spatial information, providing a relevant data source for producing LULC maps. Through the analysis of 262 papers published from 2012 to 2023, this work provides a comprehensive overview of the field, including prominent researchers, key areas of study, major CGI data sources, mapping methods, and the scope of LULC research. Additionally, it evaluates the pros and cons of various data sources and mapping methods. The findings reveal that while applying CGI with LULC labels is a common way by using spatial analysis, it is limited by incomplete CGI coverage and other data quality issues. In contrast, extracting semantic features from CGI for LULC interpretation often requires integrating multiple CGI datasets and remote sensing imagery, alongside advanced methods such as ensemble and deep learning. The paper also delves into the challenges posed by the quality of CGI data in LULC mapping and explores the promising potential of introducing large language models to overcome these hurdles.

This research explores the design and evaluation of visualization techniques of targets that reside outside of users' view and/or are occluded by other elements within a virtual reality environment (VE). We first compare four techniques (3DWedge, 3DArrow, 3DMinimap, and Radar) that use different types of visual elements to provide direction and distance information of targets. To give structure to their evaluation, we also develop a framework of four tasks (one for direction and three for distance) and their assessment criteria. The results of the first study show that 3DWedge is the best-performing and most usable technique. However, all techniques, including 3DWedge, have poor performance in dense scenarios with a large number of targets. To improve support in dense scenarios, a fifth technique, 3DWedge+, is developed by using 3DWedge as its foundation and including additional visual elements that are derived from the other three techniques which are shown to be useful. A second study is conducted to evaluate the performance of 3DWedge+ in relation to the other techniques. The results show that both 3DWedge and 3DWedge+ are significantly better in distinguishing user-to-target distance and that 3DWedge+ is particularly suitable for dense scenarios. Based on these results, we provide a set of recommendations for the design of visualization techniques of off-screen and occluded targets in 3D VE.

In daily life, deliberation is a common behavior for human to improve or refine their work (e.g., writing, reading and drawing). To date, encoder-decoder framework with attention mechanisms has achieved great progress for image captioning. However, such framework is in essential an one-pass forward process while encoding to hidden states and attending to visual features, but lacks of the deliberation action. The learned hidden states and visual attention are directly used to predict the final captions without further polishing. In this paper, we present a novel Deliberate Residual Attention Network, namely DA, for image captioning. The first-pass residual-based attention layer prepares the hidden states and visual attention for generating a preliminary version of the captions, while the second-pass deliberate residual-based attention layer refines them. Since the second-pass is based on the rough global features captured by the hidden layer and visual attention in the first-pass, our DA has the potential to generate better sentences. We further equip our DA with discriminative loss and reinforcement learning to disambiguate image/caption pairs and reduce exposure bias. Our model improves the state-of-the-arts on the MSCOCO dataset and reaches 37.5% BELU-4, 28.5% METEOR and 125.6% CIDEr. It also outperforms the-state-ofthe-arts from 25.1% BLEU-4, 20.4% METEOR and 53.1% CIDEr to 29.4% BLEU-4, 23.0% METEOR and 66.6% on the Flickr30K dataset.

Understanding the endpoint distribution of pointing selection tasks can reveal the underlying patterns on how users tend to acquire a target, which is one of the most essential and pervasive tasks in interactive systems. It could further aid designers to create new graphical user interfaces and interaction techniques that are optimized for accuracy, efficiency, and ease of use. Previous research has explored the modeling of endpoint distribution outside of virtual reality (VR) systems that have shown to be useful in predicting selection accuracy and guide the design of new interactive techniques. This work aims at developing an endpoint distribution of selection tasks for VR systems which has resulted in EDModel , a novel model that can be used to predict endpoint distribution of pointing selection tasks in VR environments. The development of EDModel is based on two users studies that have explored how factors such as target size, movement amplitude, and target depth affect the endpoint distribution. The model is built from the collected data and its generalizability is subsequently tested in complex scenarios with more relaxed conditions. Three applications of EDModel inspired by previous research are evaluated to show the broad applicability and usefulness of the model: correcting the bias in Fitts's law, predicting selection accuracy, and enhancing pointing selection techniques. Overall, EDModel can achieve high prediction accuracy and can be adapted to different types of applications in VR.

We present PizzaText, a circular keyboard layout technique for text entry in virtual reality (VR) environments that uses the dual thumbsticks of a hand-held game controller. Text entry is a common activity in VR environments but remains challenging with existing techniques and keyboard layouts that is largely based on QWERTY. Our technique makes text entry simple, easy, and efficient, even for novice users. The technique uses a hand-held controller because it is still an important input device for users to interact with VR environments. To allow rapid search of characters, PizzaText divides a circle into slices and each slice contains 4 characters. To enable fast selection, the user uses the right thumbstick for traversing the slices, and the left thumbstick for choosing the letters. The design of PizzaText is based on three criteria: efficiency, learnability, and ease-of-use. In our first study, six potential layouts are considered and evaluated. The results lead to a design with 7 slices and 4 letters per slice. The final design is evaluated in a five-day study with 10 participants. The results show that novice users can achieve an average of 8.59 Words per Minute (WPM), while expert users are able to reach 15.85 WPM, with just two hours of training.