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Timely and accurate detection of hurricane debris is critical for effective disaster response and community resilience. While post‐disaster aerial imagery is readily available, robust debris segmentation solutions applicable across multiple disaster regions remain limited. Since environmental and imaging conditions vary by region and alter debris's visual signatures, developing a debris segmentation model suitable for different hazard scenarios is challenging, especially given the scarcity of training data. This study addresses these challenges by relying on vast pretrained knowledge from the foundation vision model Contrastive Language‐Image Pre‐Training (CLIP) and fine‐tuning a lightweight decoder on labeled debris imagery, thereby achieving robust performance in segmenting non‐vegetative debris within complex urban backgrounds with a relatively small, high‐quality dataset. To enable fine‐tuning, this work introduces an open‐source dataset comprising approximately 1200 manually annotated aerial RGB images of structural debris from Hurricanes Ian, Ida, and Ike. To mitigate human biases and enhance data quality, labels from multiple annotators are strategically aggregated into consensus annotations. The resulting fine‐tuned model, named CLIPSeg‐debris, achieves a Dice score of 0.86 on data from Hurricane Ida—a disaster event entirely excluded during training—outperforming baseline models. This approach thereby enables broader adoption across multiple hazard scenarios and effectively adapts to various backgrounds, sensor resolutions, and debris conditions. 

Event cameras, which feature pixels that independently respond to changes in brightness, are becoming increasingly popular in high- speed applications due to their lower latency, reduced bandwidth requirements, and enhanced dynamic range compared to traditional frame- based cameras. Numerous imaging and vision techniques have leveraged event cameras for high- speed scene understanding by capturing high- framerate, high- dynamic range videos, primarily utilizing the temporal advantages inherent to event cameras. Additionally, imaging and vision techniques have utilized the light field—a complementary dimension to temporal information—for enhanced scene understanding.In this work, we propose "Event Fields", a new approach that utilizes innovative optical designs for event cameras to capture light fields at high speed. We develop the underlying mathematical framework for Event Fields and introduce two foundational frameworks to capture them practically: spatial multiplexing to capture temporal derivatives and temporal multiplexing to capture angular derivatives. To realize these, we design two complementary optical setups— one using a kaleidoscope for spatial multiplexing and another using a galvanometer for temporal multiplexing. We evaluate the performance of both designs using a custom-built simulator and real hardware prototypes, showcasing their distinct benefits. Our event fields unlock the full advantages of typical light fields—like post- capture refocusing and depth estimation—now supercharged for high- speed and high- dynamic range scenes. This novel light- sensing paradigm opens doors to new applications in photography, robotics, and AR/VR, and presents fresh challenges in rendering and machine learning. 

Optics that allow us to see clearly along one viewing direction while obscuring others’ view of us are useful in numerous settings, including privacy-preserving window screens and one-way mirrors for psychological studies. Additionally, due to the rise of cameras that are able to see outside the visible spectrum, there is a need for optics that can also provide one-way visibility at these wavelengths. This is particularly challenging for thermal (i.e., infrared) imaging because most existing methods require precise control of scene illumination, which is difficult to achieve in the infrared. To address this challenge, we demonstrate broadband, passive one-way visibility by precisely tuning the position and optical parameters of a single optical scatterer. We show the benefits of our approach in both a simulated and an experimental testbed. With experimental data, we demonstrate a 5.22× and 5.23× improvement in the degree of asymmetry for midwave infrared (MWIR) and visible (VIS) wavelengths, respectively. Ultimately, our method introduces a robust, passive one-way visibility system at midwave infrared (MWIR), which can aid in numerous privacy preservation applications. 

Metasurfaces have recently risen to prominence in optical research, providing unique functionalities that can be used for imaging, beam forming, holography, polarimetry, and many more, while keeping device dimensions small. Despite the fact that a vast range of basic metasurface designs has already been thoroughly studied in the literature, the number of metasurfacerelated papers is still growing at a rapid pace, as metasurface research is now spreading to adjacent fields, including computational imaging, augmented and virtual reality, automotive, display, biosensing, nonlinear, quantum and topological optics, optical computing, and more. At the same time, the ability of metasurfaces to perform optical functions in much more compact optical systems has triggered strong and constantly growing interest from various industries that greatly benefit from the availability of miniaturized, highly functional, and efficient optical components that can be integrated in optoelectronic systems at low cost. This creates a truly unique opportunity for the field of metasurfaces to make both a scientific and an industrial impact. The goal of this Roadmap is to mark this “golden age” of metasurface research and define future directions to encourage scientists and engineers to drive research and development in the field of metasurfaces toward both scientific excellence and broad industrial adoption. 

Foveated imaging provides a better tradeoff between situational awareness (field of view) and resolution, and is critical in long wavelength infrared regimes because of the size, weight, power, and cost of thermal sensors. We demonstrate computational foveated imaging by exploiting the ability of a meta-optical frontend to discriminate between different polarization states and a computational backend to reconstruct the captured image/video. The frontend is a three-element optic: the first element, which we call the “foveal” element, is a metalens that focuses s-polarized light at a distance off₁without affecting the p-polarized light; the second element, which we call the “perifovea” element, is another metalens that focuses p-polarized light at a distance off₂without affecting thes-polarized light. The third element is a freely rotating polarizer that dynamically changes the mixing ratios between the two polarization states. Both the foveal element (focal length=150mm; diameter=75mm) and the perifoveal element (focal length=25mm; diameter=25mm) were fabricated as polarization-sensitive, all-silicon, meta surfaces resulting in a large-aperture, 1:6 foveal expansion, thermal imaging capability. A computational backend then utilizes a deep image prior to separate the resultant multiplexed image or video into a foveated image consisting of a high resolution center and a lower-resolution large field of view context. We build a prototype system and demonstrate 12 frames per second real-time, thermal, foveated image and video capture..