Multi-Week, In-Class Deployments of Telepresence Robots With Four Homebound K-12 Students: Benefits, Challenges, and Recommendations

Missing significant amounts of school during K-12 education is known to put students' cognitive and social development at risk. Alternatives such as home instruction and online learning are common, but lack sufficient interaction with peers and teachers in the classroom. Mobile remote presence systems, or telepresence robots, are promising for homebound students because they provide embodiment and mobility in addition to the real-time participation offered by video conferencing technologies. Research is needed, however, for telepresence robots to meet the complex needs of homebound students participating remotely in the K-12 classroom context. We present findings from four multi-week deployments with homebound K-12 students attending classes via telepresence robots. The homebound students' experiences were documented in a total of 15 interviews and analyzed qualitatively as case studies. The homebound student participants and their deployment contexts differed from one another along multiple dimensions, and while some benefits of mobile remote attendance were enjoyed by all participants, each participant also experienced unique benefits. Some challenges with hearing, seeing, and moving the robot around the classroom warranted improvements to the design of the telepresence system. Other challenges suggested priorities for managing a classroom deployment, such as ensuring that the remote student is included in classroom activities, accountable to the teacher, and treated with respect by classmates. Based on insights from the study, we make recommendations for real-world deployment procedures in similar contexts.

F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation

Semantic segmentation of ultra-high-resolution (UHR) remote sensing imagery is critical for applications like environmental monitoring and urban planning but faces computational and optimization challenges. Conventional methods either lose fine details through downsampling or fragment global context via patch processing. While multi-branch networks address this trade-off, they suffer from computational inefficiency and conflicting gradient dynamics during training. We propose F2Net, a frequency-aware framework that decomposes UHR images into high- and low-frequency components for specialized processing. The high-frequency branch preserves full-resolution structural details, while the low-frequency branch processes downsampled inputs through dual sub-branches capturing short- and long-range dependencies. A Hybrid-Frequency Fusion module integrates these observations, guided by two novel objectives: Cross-Frequency Alignment Loss ensures semantic consistency between frequency components, and Cross-Frequency Balance Loss regulates gradient magnitudes across branches to stabilize training. Evaluated on DeepGlobe and Inria Aerial benchmarks, F2Net achieves state-of-the-art performance with mIoU of 80.22 and 83.39, respectively. Our code will be publicly available.