Deploying language-driven agents in robotics requires evaluations that reflect real-world task demands: natural-language instructions with reproducible outcomes. Such agents must connect language models to callable perception and control tools, and be assessed using deployment-critical metrics including latency, accuracy, and error modes. We present SCOPE (Simulation and Camera Operations for Perception and Evaluation), a modular agent for natural-language, open-vocabulary pan–tilt–zoom (PTZ) camera control and visual scene understanding, designed explicitly for edge deployment. SCOPE operates both in a Blender-based simulation environment and on a physical PTZ camera, executing all perception, planning, and control locally at the deployment site using edge-accessible compute.

Low Earth Orbit (LEO) satellite constellations, led by deployments such as Starlink, are playing an increasingly pivotal role in enabling global broadband connectivity. However, the reliability and performance of these space-based networks are highly sensitive to environmental dynamics, particularly localized weather phenomena that exhibit strong spatio-temporal variability. In this study, we present a continental-scale geospatial analysis of weather-induced performance degradation in the Starlink LEO network, with a focus on the contiguous United States.

Contextual encoding represents information using data attributes that collectively form context. In case of geospatial data, these attributes may include color, texture, and structure attributes of geospatial entities. This paper introduces the paradigm of contextual encoding. It then presents algorithms for generating contextual encoding of geospatial data by automatically deriving salient visual cues such as color, texture, and structure of geospatial entities. The derived encoding can then be used for mapping applications such as generating rich maps, routing directions, location search, clustering, navigation, and reverse geocoding.

Oblique images are aerial photographs taken at oblique angles to the earth's surface. Projections of vector and other geospatial data in these images depend on camera parameters, positions of the entities, surface terrain, and visibility. This paper presents a robust and scalable algorithm to detect inconsistencies in vector data using oblique images. The algorithm uses image descriptors to encode the local appearance of a geospatial entity in images. These image descriptors combine color, pixel-intensity gradients, texture, and steerable filter responses. A Support Vector Machine classifier is trained to detect image descriptors that are not consistent with underlying vector data, digital elevation maps, building models, and camera parameters. In this paper, we train the classifier on visible road segments and non-road data. Thereafter, the trained classifier detects inconsistencies in vectors, which include both occluded and misaligned road segments. The consistent road segments validate our vector, DEM, and 3-D model data for those areas while inconsistent segments point out errors. We further show that a search for descriptors that are consistent with visible road segments in the neighborhood of a misaligned road yields the desired road alignment that is consistent with pixels in the image.

A surface is often modeled as a triangulated mesh of 3D points and textures associated with faces of the mesh. The 3D points could be either sampled from range data or derived from a set of images using a stereo or Structurefrom- Motion algorithm. When the points do not lie at critical points of maximum curvature or discontinuities of the real surface, faces of the mesh do not lie close to the modeled surface. This results in textural artifacts, and the model is not perfectly coherent with a set of actual images—the ones that are used to texture-map its mesh. This paper presents a technique for perfecting the 3D surface model by repositioning its vertices so that it is coherent with a set of observed images of the object. The textural artifacts and incoherence with images are due to the non-planarity of a surface patch being approximated by a planar face, as observed from multiple viewpoints. Image areas from the viewpoints are used to represent texture for the patch in eigenspace. The eigenspace representation captures variations of texture, which we seek to minimize. A coherence measure based on the difference between the face textures reconstructed from eigenspace and the actual images is used to reposition the vertices so that the model is improved or faired. We refer to this technique of model refinement as EigenFairing, by which the model is faired, both geometrically and texturally, to better approximate the real surface.