The paper reports the first on‑board detection of methane point source emissions using imaging spectroscopy data from the AVIRIS‑5 sensor during the Tokyo Field Campaign in March 2026. It describes how a compact machine‑learning model was deployed on the aircraft to predict potential trace‑gas events in real time, circumventing communication bottlenecks that prevent full data downlink during flight. The approach enables immediate identification of transient gas releases without waiting for ground‑based processing.
arXiv:2604. 10094v2 Announce Type: replace-cross Abstract: Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies.
The paper introduces UnorthoDOS, a dataset and machine‑learning approach that enables methane plume detection directly on unorthorectified hyperspectral satellite imagery. Using U‑Net models trained on this data, the authors achieve performance close to models trained on orthorectified images (IoU 16.91% vs. 18.47%) and far surpass the traditional matched‑filter baseline (IoU 4.76%). They also demonstrate that FP16 compression can reduce model size by half with negligible loss in output accuracy, making onboard deployment feasible.
arXiv:2609.09762v1 Announce Type: new Abstract: Methane plume detection from satellite imagery is constrained by incomplete observations: public satellites provide complementary spatial, spectral, an...
arXiv:2608. 07580v1 Announce Type: cross Abstract: In this work, we present a wildfire temperature retrieval framework for VSWIR imaging spectroscopy data, employed on data from NASA's Airborne Visible Infrared Imaging Spectrometer (AVIRIS-3).
arXiv:2512. 23234v3 Announce Type: replace-cross Abstract: Infrared gas leak detection is important for industrial safety and environmental monitoring, but automatic detection remains challenging because gas plumes are often faint, small, semi-transparent, and weakly bounded.
The paper presents an AI-based system for onboard maritime object detection on Earth‑observation satellites, aiming to reduce data volume by selectively downlinking relevant vessel information. A YOLOX‑S detector is trained on a dataset of 68 Maxar scenes with 43 vessel classes, then quantized and deployed on a Versal VC1902 DPU with minimal loss in accuracy and a few‑second processing time per scene. Various downlink modes—metadata only, image crops, tiles, or full scenes—are evaluated, showing that tiles retain 98% of vessels while reducing scene volume to 29%, and crops retain 83% with only 3% of the volume.
The paper proposes expanding technosignature searches beyond radio emissions to include surface technosignatures—intentional, non-random patterns on planetary terrain. It argues that on geologically inactive bodies like the Moon and Mars, such artificial traces could persist for hundreds of millions of years and can be detected using high-resolution imaging (e.g., HiRISE) combined with machine‑learning anomaly‑detection. The authors outline supervised and unsupervised modeling approaches, and suggest that complementary LiDAR, radar, and spectroscopy could provide quantitative limits on surface technosignatures while benefiting planetary science.
HiPredict is a machine‑learning framework that reconstructs the failing RED4 spectral channel of the HiRISE instrument on the Mars Reconnaissance Orbiter using a U‑Net model trained on IR10, BG12, neighboring red‑channel data, and acquisition metadata. The model, trained on 81,717 samples and evaluated on 23,348 test samples, achieves a Normalised Mean Absolute Error of 1.1%, outperforming the existing synthetic red (SYN4) approach (3.1%). The reconstructed RED4 images enhance colour‑image recovery and better represent Martian surface features such as icy craters, ridge‑crest stratigraphy, and chloride deposits.
arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.
arXiv:2606. 08324v1 Announce Type: cross Abstract: Passive long-wave infrared (LWIR) hyperspectral imaging under a standoff geometry depends on atmospheric absorption and emission, as well as reflected radiance, thus making atmospheric compensation essential to get knowledge of a target of interest.
arXiv:2601.23276v4 Announce Type: replace-cross Abstract: Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts b...
The paper investigates how raw or minimally processed satellite imagery affects onboard AI object detection for space missions. By systematically degrading Very High Resolution Maxar images in terms of Signal‑to‑Noise Ratio, Modulation Transfer Function, and Ground Sampling Distance, the authors evaluate three lightweight detectors—YOLOv5s, YOLOX‑S, and NanoDet—on the resulting data. Results show that image quality impacts detection performance in a degradation‑specific way, with GSD consistently shifting performance, while MTF and SNR effects vary by model and resolution; severe blur‑plus‑noise combinations cause the greatest losses.