arXiv:2511.18521v1 Announce Type: new 
Abstract: Geostationary hyperspectral satellites generate terabytes of data daily, creating critical challenges for storage, transmission, and distribution to the scientific community. We present a variational autoencoder (VAE) approach that achieves x514 compression of NASA's TEMPO satellite hyperspectral observations (1028 channels, 290-490nm) with reconstruction errors 1-2 orders of magnitude below the signal across all wavelengths. This dramatic data volume reduction enables efficient archival and sharing of satellite observations while preserving spectral fidelity. Beyond compression, we investigate to what extent atmospheric information is retained in the compressed latent space by training linear and nonlinear probes to extract Level-2 products (NO2, O3, HCHO, cloud fraction). Cloud fraction and total ozone achieve strong extraction performance (R^2 = 0.93 and 0.81 respectively), though these represent relatively straightforward retrievals given their distinct spectral signatures. In contrast, tropospheric trace gases pose genuine challenges for extraction (NO2 R^2 = 0.20, HCHO R^2 = 0.51) reflecting their weaker signals and complex atmospheric interactions. Critically, we find the VAE encodes atmospheric information in a semi-linear manner - nonlinear probes substantially outperform linear ones - and that explicit latent supervision during training provides minimal improvement, revealing fundamental encoding challenges for certain products. This work demonstrates that neural compression can dramatically reduce hyperspectral data volumes while preserving key atmospheric signals, addressing a critical bottleneck for next-generation Earth observation systems. Code - https://github.com/cfpark00/Hyperspectral-VAE

تم تطوير نهج جديد يستخدم الشبكات التلقائية المتغيرة (VAEs) لضغط البيانات الطيفية من قمر TEMPO الصناعي التابع لناسا، محققًا نسبة ضغط ملحوظة تبلغ x514 مع الحفاظ على دقة طيفية عالية. تقلل هذه الطريقة بشكل كبير من حجم البيانات التي تنتجها الأقمار الصناعية الثابتة، والتي يمكن أن تنتج تيرابايت من البيانات يوميًا.

Se ha desarrollado un nuevo enfoque que utiliza autoencoders variacionales (VAE) para comprimir datos hiperespectrales del satélite TEMPO de la NASA, logrando una notable relación de compresión de x514 mientras mantiene una alta fidelidad espectral. Este método reduce significativamente el volumen de datos generado por los satélites geoestacionarios, que pueden producir terabytes de datos diariamente.

Une nouvelle approche utilisant des autoencodeurs variationnels (VAE) a été développée pour compresser les données hyperspectrales du satellite TEMPO de la NASA, atteignant un rapport de compression remarquable de x514 tout en maintenant une haute fidélité spectrale. Cette méthode réduit considérablement le volume de données généré par les satellites géostationnaires, qui peuvent produire des téraoctets de données quotidiennement.

A new approach utilizing variational autoencoders (VAEs) has been developed to compress hyperspectral data from NASA's TEMPO satellite, achieving a remarkable x514 compression ratio while maintaining high spectral fidelity. This method significantly reduces the data volume generated by geostationary satellites, which can produce terabytes of data daily.

Hyperspectral Variational Autoencoders for Joint Data Compression and Component Extraction

arXiv:2512.03257v1 Announce Type: cross 
Abstract: Rapid and accurate wildfire detection is crucial for emergency response and environmental management. In airborne and spaceborne missions, real-time algorithms must distinguish between no fire, active fire, and post-fire conditions, and estimate fire intensity. Multispectral and hyperspectral thermal imagers provide rich spectral information, but high data dimensionality and limited onboard resources make real-time processing challenging. As wildfires increase in frequency and severity, the need for low-latency and computationally efficient onboard detection methods is critical.
  We present a systematic evaluation of multiple deep learning architectures, including custom Convolutional Neural Networks (CNNs) and Transformer-based models, for multi-class fire classification. We also introduce PyroFocus, a two-stage pipeline that performs fire classification followed by fire radiative power (FRP) regression or segmentation to reduce inference time and computational cost for onboard deployment. Using data from NASA's MODIS/ASTER Airborne Simulator (MASTER), which is similar to a next-generation fire detection sensor, we compare accuracy, inference latency, and resource efficiency.
  Experimental results show that the proposed two-stage pipeline achieves strong trade-offs between speed and accuracy, demonstrating significant potential for real-time edge deployment in future wildfire monitoring missions.

تم تقديم PyroFocus كحل يعتمد على التعلم العميق للكشف عن حرائق الغابات في الوقت الحقيقي باستخدام صور الاستشعار عن بعد متعددة الأطياف. تهدف هذه الطريقة المبتكرة إلى تحسين دقة وسرعة تحديد ظروف الحريق، وهو أمر حاسم للاستجابة الفعالة للطوارئ وإدارة البيئة.

PyroFocus se ha presentado como una solución de aprendizaje profundo para la detección en tiempo real de incendios forestales utilizando imágenes de teledetección multiespectral. Este enfoque innovador tiene como objetivo mejorar la precisión y la velocidad en la identificación de condiciones de incendio, lo cual es crucial para una respuesta de emergencia efectiva y la gestión ambiental.

PyroFocus a été introduit comme une solution d'apprentissage profond pour la détection en temps réel des incendies de forêt à l'aide d'images de télédétection multispectrales. Cette approche innovante vise à améliorer l'exactitude et la rapidité d'identification des conditions d'incendie, ce qui est crucial pour une réponse d'urgence efficace et la gestion environnementale.

PyroFocus has been introduced as a deep learning solution for real-time wildfire detection using multispectral remote sensing imagery. This innovative approach aims to enhance the accuracy and speed of identifying fire conditions, which is critical for effective emergency response and environmental management.

PyroFocus: A Deep Learning Approach to Real-Time Wildfire Detection in Multispectral Remote Sensing Imagery

arXiv:2512.03729v1 Announce Type: cross 
Abstract: The US Naval Research Laboratory's (NRL's) Autonomous Planning In-space Assembly Reinforcement-learning free-flYer (APIARY) experiment pioneers the use of reinforcement learning (RL) for control of free-flying robots in the zero-gravity (zero-G) environment of space. On Tuesday, May 27th 2025 the APIARY team conducted the first ever, to our knowledge, RL control of a free-flyer in space using the NASA Astrobee robot on-board the International Space Station (ISS). A robust 6-degrees of freedom (DOF) control policy was trained using an actor-critic Proximal Policy Optimization (PPO) network within the NVIDIA Isaac Lab simulation environment, randomizing over goal poses and mass distributions to enhance robustness. This paper details the simulation testing, ground testing, and flight validation of this experiment. This on-orbit demonstration validates the transformative potential of RL for improving robotic autonomy, enabling rapid development and deployment (in minutes to hours) of tailored behaviors for space exploration, logistics, and real-time mission needs.

نجح مختبر الأبحاث البحرية الأمريكية في تجربة APIARY في إظهار أول تحكم باستخدام التعلم المعزز لروبوت طائر حر في الفضاء، باستخدام روبوت Astrobee التابع لناسا على متن محطة الفضاء الدولية في 27 مايو 2025. تضمنت هذه التجربة تدريب سياسة تحكم قوية في بيئة محاكاة، مما عزز قدرة الروبوت على العمل بشكل مستقل في ظروف انعدام الجاذبية.

El experimento APIARY del Laboratorio de Investigación Naval de EE. UU. demostró con éxito el primer control de un robot volador libre en el espacio mediante aprendizaje por refuerzo, utilizando el robot Astrobee de la NASA a bordo de la Estación Espacial Internacional el 27 de mayo de 2025. Este experimento implicó el entrenamiento de una política de control robusta en un entorno simulado, mejorando la capacidad del robot para operar de manera autónoma en condiciones de microgravedad.

L'expérience APIARY du Laboratoire de recherche naval des États-Unis a réussi à démontrer le premier contrôle par apprentissage par renforcement d'un robot volant libre dans l'espace, en utilisant le robot Astrobee de la NASA à bord de la Station spatiale internationale le 27 mai 2025. Cette expérience a impliqué l'entraînement d'une politique de contrôle robuste dans un environnement simulé, améliorant la capacité du robot à fonctionner de manière autonome en conditions de microgravité.

The US Naval Research Laboratory's APIARY experiment successfully demonstrated the first reinforcement learning control of a free-flying robot in space using the NASA Astrobee on the International Space Station on May 27, 2025. This experiment involved training a robust control policy in a simulated environment, enhancing the robot's ability to operate autonomously in zero-gravity conditions.

Hyperspectral Variational Autoencoders for Joint Data Compression and Component Extraction

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