New methods detect roads in low-res satellite data, overcoming visibility challenges.
problem Detecting roads in low-resolution satellite imagery, especially those hard to see.
method Two deep learning frameworks for ordinal classification of road types from satellite time series data.
result Models can identify large and medium-sized roads from Sentinel-2 imagery.
Self-attention improves satellite time series classification without preprocessing.
problem Efficiently classifying raw satellite time series data.
method Comparison of deep learning models including self-attention, 1D-convolutions, recurrence, and random forest.
result Self-attention and recurrent neural networks outperform convolutional neural networks on raw satellite time series.
A cased-based reasoning method predicts rare events on strategic sites using satellite imagery.
problem Manual prediction of rare events on strategic sites is impractical due to large datasets.
method Case-based reasoning approach incorporating expert knowledge for irregular time series and small datasets.
result The method significantly outperforms random selection on challenging applications.
Generative adversarial approach for satellite image time series land cover classification.
problem Enhance interpretability of land cover classification models.
method Generative adversarial counterfactual approach for multi-class land cover classification.
result Discovery of interesting information on land cover class relationships and sparser, interpretable solutions.
Study uses satellite data to predict tailings dam collapse risk.
problem Detecting early signs of tailings dam instability.
method Spectral analysis of satellite InSAR displacement time series data.
result Algorithm detects risk milestones up to 5 months before dam collapse.
The study forecasts water quality from satellite data using machine learning.
problem Predicting future water quality from satellite data for coastal regions.
method Decomposed time series into components and used machine learning models (SARIMA, regression, neural network).
result Regression and neural network models are best at predicting Chl-a, SARIMA model best at FLH and SST.
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…
Probabilistic NDVI forecasting from sparse satellite data.
problem Challenges in short-term NDVI forecasting due to sparse and irregular satellite data.
method Probabilistic forecasting framework using historical NDVI and meteorological observations, with temporal-distance weighted quantile loss and extreme-weather feature engineering.
result The proposed method outperforms baselines on pointwise and probabilistic evaluation metrics.
Method quantifies forest cover changes due to human activities using satellite data.
problem Quantify forest cover changes due to human activities.
method Computer vision, machine learning/deep learning, Bayesian structural causal model.
result Causal inference of forest cover changes due to human activities.
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use this time series data to make predictions at a large scale, for instance, to produce up-to-date crop cover maps. Most time series classificati…
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring. It augments existing classification models by an additional stopping probability based on the previously seen information. This mechanism is end-to-end trainable and derives its stopping decision s…
New method detects TC imagery patterns for rapid intensity change.
problem Detecting upcoming rapid intensity changes in TC satellite imagery.
method Nonparametric test of association between images and event labels using neural networks and bootstrap.
result Identifies archetypes of infrared imagery associated with elevated rapid intensification risk.
Bayesian deep learning predicts satellite collisions.
problem Space debris poses planetary risk.
method Bayesian deep learning with LSTM networks.
result Predicts conjunction event evolution with uncertainties.
New series invariant for knots and cables, with robustness and relations.
problem Computing series invariants for complex knots and cables.
method Explicit computation and analysis of satellite knots, including a cable of the figure eight knot.
result First example of a cable knot with more than ten crossings, demonstrating robustness and integrality.
Generative model downgrades coarse satellite images to fine resolution.
problem Reconstructing fine resolution satellite images from coarse scale inputs.
method Combines U-Net transfer encoder with diffusion-based generative model.
result Excellent performance (R2 = 0.65 to 0.94) across seasonal regional splits.
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosphere as well as bottom-of-atmosphere time series in the region of Brittany (Breizh in local language), north-east France. We compare seven r…
FedSpace optimizes ML training on satellites and ground stations.
problem Training machine learning models on satellites with limited bandwidth.
method Federated Learning framework that dynamically schedules model aggregation based on satellite orbits.
result Reduces training time by 1.7 days over state-of-the-art algorithms.
Satellite knots have a higher trunk number than their base knots.
problem Understanding the relationship between satellite knots and their base knots.
method Using the Thurston norm and properties of satellite patterns.
result The trunk number of satellite knots is strictly greater than the product of the Thurston norm and the trunk number of their base knots.
Transformer model removes noise from light curves efficiently.
problem Challenges in processing astrophysical light curves due to noise.
method Denoising Time Series Transformer (DTST) model trained with masked objective.
result DTST model excels at removing noise and outliers in time series datasets.
Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-var…
AIKAE enhances IKAE for long-term time series forecasting.
problem Limitation of dimension conservation in IKAE models.
method Augmented with a non-invertible encoder network.
result AIKAE improves long-term forecasting accuracy.
New method maps land cover using radar and optical satellite images.
problem Efficiently exploiting multiple sources of information for land cover mapping.
method Deep learning framework with attention mechanism and pretraining strategy.
result Attention mechanism and extended RNN model outperform competitors.
The paper analyzes optimal overbetting strategies for a satellite investment account.
problem Optimal control of leverage in a satellite investment account with limited leverage.
method Recursive overbetting strategy to maximize growth rate, solved via HJB equation.
result Optimal overbetting strategy balances growth rate of satellite and composite bankroll.
We show that the crossing number of a satellite knot is at least 10^{-13} times the crossing number of its companion knot.
Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
problem Accurately estimate and predict AOD propagation from wildfires using multi-source satellite data.
method Physics-informed statistical modeling integrating multi-source satellite data with an advection-diffusion equation.
result The proposed approach accurately predicts AOD propagation and demonstrates model interpretability.
Any knot in a solid torus, called a pattern or satellite operator, acts on knots in the 3-sphere via the satellite construction. We introduce a generalization of satellite operators which form a group (unlike traditional satellite operators), modulo a generalization of concordance. This group has an action on the set o…
Study uses cGAN to translate multispectral to nighttime satellite imagery.
problem Limited understanding of nighttime satellite imagery composition.
method Adopted and modified pix2pix cGAN framework for multispectral-to-nighttime translation.
result Proves feasibility of multispectral-to-nighttime translation.
Space debris warnings follow a predictable pattern, allowing timely satellite maneuvers.
problem Estimating when fresh information about space debris will arrive.
method Statistical learning model of the message arrival process, specifically a Bayesian Poisson process.
result The average prediction error for the next message arrival time is smaller than baseline predictions.
A new clustering algorithm tracks satellite hotspot data for bushfire tracking.
problem Tracking bushfires using satellite data.
method Spatiotemporal clustering algorithm with adjustable parameters.
result The algorithm effectively clusters satellite hotspot data for tracking bushfires.
Enhances solar flare prediction with advanced preprocessing and contrastive learning.
problem Accurate prediction of solar flares to mitigate risks to astronauts and equipment.
method Advanced data preprocessing pipeline and contrastive learning with GRU regression model.
result Exceptional True Skill Statistic (TSS) scores, surpassing previous methods.
Machine learning detects building damage in satellite images.
problem Extracting damage information from satellite imagery is slow and labor-intensive.
method Used four convolutional neural network models to detect damaged buildings.
result Models performed well in detecting damaged buildings in the 2010 Haiti earthquake.
Study of knots with generalized Mazur patterns and their invariants.
problem Understanding the invariants and properties of knots with generalized Mazur patterns.
method Computational analysis of τ and ε invariants for n-twisted satellites. result None of the n-twisted patterns from the family act surjectively on the smooth or rational concordance group. Formula for τ-invariant of satellite knots derived from L-space satellite operators.
problem Calculating the τ-invariant of satellite knots using L-space satellite operators.
method Algorithm to compute knot Floer complexes and formula for τ-invariant.
result Formula recovers existing formulas and proves new properties of τ-invariant.
Estimates spatio-temporal data with satellite NO2 concentrations using Yule-Walker equations.
problem Estimating large spatio-temporal autoregressions with unknown spatial interactions.
method Sparse generalized Yule-Walker estimation, penalized regression, spatial and temporal dependence.
result Strong forecast improvements and evidence of spatial interactions in NO2 satellite data.
In this paper, we construct two families of satellite constructions for Brunnian links, called the satellite sum and the satellite tie. An interesting fact is that by applying the satellite sum and the satellite tie constructions, we can build infinitely many new Brunnian links from any given Brunnian links. With the h…
Satellite knots can be trivialized by a single band move.
problem Satellite knots and their trivialization.
method Infinite family of satellite knots and a single band move.
result No disjoint band unknotting exists for satellite knots.
This study analyzes satellite communication latency using a stochastic geometry model.
problem Latency analysis of LEO satellite relay communication systems.
method Stochastic geometry framework with spherical BPP models, suboptimal satellite relay selection strategy.
result Derives distance distributions and analytical expressions for transmission delays.
Satellite links of fully positive braids are characterized.
problem Characterizing satellite links of fully positive braids.
method Analyzing fully positive braids and their satellites.
result Satellite links of fully positive braids are characterized by specific conditions.
Satellite links with many twists have simpler companions.
problem Relationship between satellite and companion links' complexity.
method Constructing satellite links with multiple full twists and analyzing their companion links.
result Satellite links with many twists have simpler companion links.
The Conway knot can't be smoothly tied to any other knot an infinite number of times.
problem Understanding the concordance order of knots.
method Rasmussen invariant, satellite operations, null-homologous twists.
result The Conway knot has infinite concordance order.
Develops a new framework for integrating satellite allocations in small portfolios.
problem Feasibility constraints in small portfolios, not return predictability, are the primary concerns.
method A four-layer feasibility framework: physical, economic, structural, and epistemic.
result Closed-form feasibility bounds on satellite size, turnover, and breadth without return forecasts.
Paper compares neural networks and time-series models for weather derivative pricing.
problem Pricing accuracy and regime adaptation for temperature and precipitation weather derivatives.
method Benchmarked harmonic-regression/ARMA vs. feed-forward neural network for temperature. Used CNN for precipitation, adapting to seasonal heterogeneity.
result CNN yields more accurate pricing, especially for regime-adapted seasonal data.
New proof for minimizing tunnel systems in satellite chain links.
problem Minimizing the tunnel number of satellite chain links.
method Proving the tunnel number is minimized for links with a specific number of components and bridge number.
result The result is sharp for satellite chain links over a 2-bridge knot.
The paper explores slice disks and their properties using satellite operations and knot Floer homology.
problem Tackles the conjecture about slice disks and their positive Whitehead doubles.
method Uses techniques from knot Floer homology, Seiberg-Witten theory, and Khovanov homology.
result Provides evidence for the conjecture and constructs exotic disks.
The Gluck twist preserves the diffeomorphism type of certain satellite 2-knots.
problem Preserving the diffeomorphism type of satellite 2-knots under the Gluck twist.
method Using new descriptions of satellite 2-knots, the paper shows that the Gluck twist does not change the diffeomorphism type of certain satellite 2-knots in three ways.
result The Gluck twist preserves the diffeomorphism type of certain satellite 2-knots.
Researchers compute Khovanov polynomials for satellite knots.
problem Computing Khovanov polynomials for satellite knots.
method Explicit computation using a computer program for two families of satellite knots.
result Khovanov polynomials can be expressed as a linear combination of pattern and companion invariants, with a jump at a critical point.
New satellite knots counter a conjecture about Lorenz knots.
problem A conjecture about satellite knots and Lorenz knots was disproven.
method Constructed infinitely many counterexamples of satellite knots that are not cables.
result The conjecture was amended and shown to hold for many Lorenz knots.
Formula for satellite operators using knot Floer homology.
problem Computing knot Floer homology for satellite knots.
method Using Heegaard Floer Dehn surgery formulas and formal knot Floer complexes.
result Formulas to compute knot Floer homology for satellite knots.