arXiv Machine Learning
Aug 11
A Machine Learning Based Search for Lunar Anomalies
arXiv:2608. 09350v1 Announce Type: cross Abstract: The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0.
Solar System Surface Technosignatures: Searching for Signs of Artificial Design on Extraterrestrial Terrain
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.
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Hugging Face Trending Papers
Aug 10
A Machine Learning Based Search for Lunar Anomalies
The Lunar Reconnaissance Orbiter (LRO) has been collecting high-resolution images (at around 0. 5-2 meters per pixel linearly with its Narrow Angle Camera) of the Moon since 2009, amassing a large dataset of images and offering researchers the opportunity to study the surface of the Moon at unprecedented scale.
arXiv Computer Vision
Sep 21
MarsFM: Shading-Regularized Flow Matching for Martian Relief Estimation
arXiv:2609.21095v1 Announce Type: new Abstract: We present MarsFM, an image-conditioned latent flow-matching model for local Martian relief estimation from single-band HiRISE RED orthoimagery. The me...
arXiv Computer Vision
Sep 1
MANTLE: A Framework for Adaptive In-Situ Planetary Perception Using a Modular Uplink Principle
MANTLE is a multi‑task adaptive network designed for planetary perception, featuring a shared DINOv2 backbone with separate heads for landform classification and boulder segmentation. Trained on HiRISE and MSL imagery, it achieved 92.56% accuracy on seven Martian terrain classes and a 0.753 IoU for boulder segmentation, with strong cross‑sol generalization. The framework follows the Modular Uplink Principle, allowing lightweight task‑specific heads to be trained on Earth and uplinked to the rover without retraining the core model.
arXiv Computer Vision
Oct 15
HyperLoop: Hyperspectral-Assisted Loop Closure Detection in 3D-SLAM for Planetary Exploration
arXiv:2610.19742v1 Announce Type: new Abstract: Autonomous navigation of planetary rovers is challenged by visually repetitive, featureless terrain. SLAM systems based on 3D Gaussian Splatting (3DGS)...
arXiv Machine Learning
Oct 19
HiPredict: Predicting the missing HiRISE RED4 spectral channel with machine learning
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 Machine Learning
Jul 30
Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
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.
arXiv Machine Learning
Jul 21
VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space
arXiv:2601. 18823v4 Announce Type: replace Abstract: Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data.
arXiv Machine Learning
Sep 1
Minerals in the Wild: A Hyperspectral-XRF Dataset for Elemental Composition Estimation
arXiv:2608.30537v1 Announce Type: cross Abstract: Rapid mineral characterization is essential for applications ranging from mineral exploration to industrial ore processing. To this end, Hyperspectra...
arXiv Machine Learning
Sep 7
On-board ML for Trace Gas detection in Imaging Spectroscopy data
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.
Hugging Face Trending Papers
Aug 31
Minerals in the Wild: A Hyperspectral-XRF Dataset for Elemental Composition Estimation
Rapid mineral characterization is essential for applications ranging from mineral exploration to industrial ore processing. To this end, Hyperspectral Imaging (HSI) has emerged as a promising sensing...
arXiv Machine Learning
Jun 24
Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks
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.