Winter officially arrives on Sunday, December 21. A view of the Geminid meteor shower and stargazing at the Tunnel View of Yosemite National Park on December 14, 2023. Breakthroughs, discoveries, and DIY tips sent every weekday. As one might expect from a month full of long, dark nights, December is a highlight for those with a penchant for looking to the stars . This year, the stargazing on offer promises to be particularly good.

Three meteor showers will peak this month. This delightfully detailed false color image of Saturn is a combination of three images taken in January 1998 by the Hubble Space Telescope and shows the ringed planet in reflected infrared light. Different colors indicated varying heights and compositions of cloud layers generally thought to consist of ammonia ice crystals. The eye-catching rings cast a shadow on Saturn's upper hemisphere, while the bright stripe seen within the left portion of the shadow is infrared sunlight streaming through the large gap in the rings known as the Cassini Division. Breakthroughs, discoveries, and DIY tips sent every weekday.

Section A.1 outlines the generation process of the SynRS3D dataset, including the tools and It also covers the licenses for these plugins. Section A.4 describes the experimental setup and the selection of hyperparameters for the RS3DAda method. Section A.5 presents the ablation study results and analysis for the RS3DAda method. Section A.6 provides supplementary experimental The generation workflow of SynRS3D involves several key steps, from initializing sensor and sunlight parameters to generating the layout, geometry, and textures of the scene. Initialization: Set up the sensor and sunlight parameters using uniform and normal distributions to simulate various conditions.

This task combines land cover mapping, which is closely related to semantic segmentation in computer vision, and height estimation.

Comet C/2025 A6 (Lemmon) was only discovered in January 2025. Breakthroughs, discoveries, and DIY tips sent every weekday. Stargazers will be happy to know that October will see the cosmos compensating for a couple of relatively lean months.There will be a whole bunch of celestial bodies to see over the next month, including the year's largest and brightest full moon, the arrival of a brand new comet, two meteor showers and a good chance to see our solar system's favorite big fella in all his glory. October's full moon finds our closest celestial companion at its perigee, i.e. the point at which it's closest to the Earth. This means that this month's full moon will be [drum roll] a supermoon!

Improving context faithfulness in large language models is essential for developing trustworthy retrieval augmented generation systems and mitigating hallucinations, especially in long-form question answering (LFQA) tasks or scenarios involving knowledge conflicts. Existing methods either intervene LLMs only at inference without addressing their inherent limitations or overlook the potential for self-improvement. In this paper, we introduce GenDiE (Generate, Discriminate, Evolve), a novel self-evolving framework that enhances context faithfulness through fine-grained sentence-level optimization. GenDiE combines both generative and discriminative training, equipping LLMs with self-generation and self-scoring capabilities to facilitate iterative self-evolution. This supports both data construction for model alignment and score-guided search during inference. Furthermore, by treating each sentence in a response as an independent optimization unit, GenDiE effectively addresses the limitations of previous approaches that optimize at the holistic answer level, which may miss unfaithful details. Experiments on ASQA (in-domain LFQA) and ConFiQA (out-of-domain counterfactual QA) datasets demonstrate that GenDiE surpasses various baselines in both faithfulness and correctness, and exhibits robust performance for domain adaptation.

Disaster events occur around the world and cause significant damage to human life and property. Earth observation (EO) data enables rapid and comprehensive building damage assessment (BDA), an essential capability in the aftermath of a disaster to reduce human casualties and to inform disaster relief efforts. Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events, mostly using optical EO data. However, solutions based on optical data are limited to clear skies and daylight hours, preventing a prompt response to disasters. Integrating multimodal (MM) EO data, particularly the combination of optical and SAR imagery, makes it possible to provide all-weather, day-and-night disaster responses. Despite this potential, the development of robust multimodal AI models has been constrained by the lack of suitable benchmark datasets. In this paper, we present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response. To the best of our knowledge, BRIGHT is the first open-access, globally distributed, event-diverse MM dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 12 regions worldwide, with a particular focus on developing countries where external assistance is most needed. The optical and SAR imagery in BRIGHT, with a spatial resolution between 0.3-1 meters, provides detailed representations of individual buildings, making it ideal for precise BDA. In our experiments, we have tested seven advanced AI models trained with our BRIGHT to validate the transferability and robustness. The dataset and code are available at https://github.com/ChenHongruixuan/BRIGHT. BRIGHT also serves as the official dataset for the 2025 IEEE GRSS Data Fusion Contest.

Space surveillance depends on efficiently directing sensor resources to maintain custody of known catalog objects. However, it remains unclear how to best utilize these resources to rapidly search for and track newly detected space objects. Provided a novel measurement, a search set can be instantiated through admissible region constraints to inform follow-up observations. In lacking well-constrained bounds, this set rapidly spreads in the along-track direction, growing much larger than a follow-up sensor's finite field of view. Moreover, the number of novel objects may be uncertain, and follow-up observations are most commonly corrupted by false positives from known catalog objects and missed detections. In this work, we address these challenges through the introduction of a joint sensor control and multi-target tracking approach. The search set associated to a novel measurement is represented by a Cardinalized Probability Hypothesis Density (CPHD), which jointly tracks the state uncertainty associated to a set of objects and a probability mass function for the true target number. In follow-up sensor scans, the information contained in an empty measurement set, and returns from both novel objects and known catalog objects is succinctly captured through this paradigm. To maximize the utility of a follow-up sensor, we introduce an information-driven sensor control approach for steering the instrument. Our methods are tested on two relevant test cases and we provide a comparative analysis with current naive tasking strategies.