Guixiang Zhang | Searcharxiv

arXiv2026

Designing Drone Interfaces to Assist Pedestrians Crossing Non-Signalised Roads

Recent research highlights the potential of drones to enhance pedestrian experiences, such as aiding navigation and supporting street-level activities. This paper explores the design of drone interfaces to assist pedestrians crossing dangerous roads without designated crosswalks or traffic lights, leveraging drones' ability to monitor and analyse real-time traffic data. Inspired by existing traffic signal systems, the interface communicates safety information through permissive alerts, prohibitive warnings, directional warnings, and collision emergency warnings. These safety cues were integrated into drone interfaces using in-situ projections and drone-equipped screens through an iterative design process. A mixed-methods, within-subjects VR evaluation (n=18) revealed that drone-assisted systems significantly improved pedestrian safety experiences and reduced mental workload compared to a baseline without any crossing aid, with projections outperforming screens. The findings suggest the potential for drone interfaces to be integrated into connected traffic systems. We also offer design recommendations for developing drone interfaces that support safe pedestrian crossings.

cs.HC↗

arXiv2023

On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data

Recent deep learning-based methods outperform traditional learning methods on remote sensing (RS) semantic segmentation/classification tasks. However, they require large training datasets and are generally known for lack of transferability due to the highly disparate RS image content across different geographical regions. Yet, there is no comprehensive analysis of their transferability, i.e., to which extent a model trained on a source domain can be readily applicable to a target domain. Therefore, in this paper, we aim to investigate the raw transferability of traditional and deep learning (DL) models, as well as the effectiveness of domain adaptation (DA) approaches in enhancing the transferability of the DL models (adapted transferability). By utilizing four highly diverse RS datasets, we train six models with and without three DA approaches to analyze their transferability between these datasets quantitatively. Furthermore, we developed a straightforward method to quantify the transferability of a model using the spectral indices as a medium and have demonstrated its effectiveness in evaluating the model transferability at the target domain when the labels are unavailable. Our experiments yield several generally important yet not well-reported observations regarding the raw and adapted transferability. Moreover, our proposed label-free transferability assessment method is validated to be better than posterior model confidence. The findings can guide the future development of generalized RS learning models. The trained models are released under this link: https://github.com/GDAOSU/Transferability-Remote-Sensing

cs.CV↗