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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,786 papers · 148 categories

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12 results for “Sentinel-1/2”

Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time serieseess.IV

Paper detects anomalies in wheat and rapeseed crops using satellite data.

problem Detecting anomalies in crop development at parcel-level.

method Unsupervised outlier detection using SAR and multispectral features.

result Best performance with a 10% outlier ratio, achieving 94.1% true positives for rapeseed and 95.5% for wheat.

crop anomaly detection multispectral SAR outlier detection Sentinel-1 Sentinel-2

2020-04-17abs ↗pdf ↗

Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networkseess.IV

Deep learning detects snow avalanches in SAR images with high accuracy.

problem Accurate detection of snow avalanches in remote areas using satellite imagery.

method Fully Convolutional Neural Network trained on manually labeled Sentinel-1 radar images.

result Deep learning model achieves F1 score above 66% compared to 38% for state-of-the-art methods.

snow avalanche Sentinel-1 SAR deep learning neural networks

2019-10-11abs ↗pdf ↗

Forest tree species classification and entropy-derived uncertainty mapping using extreme gradient boosting and Sentinel-1/2 satellite dataq-bio.QM

Forest tree species mapped with high accuracy using satellite data.

problem Classifying dominant tree species in Swedish forests.

method Extreme gradient boosting model with Bayesian optimization, combining Sentinel-1/2 satellite data and field observations.

result Overall accuracy of 85%, F1 score of 0.82, Matthews correlation coefficient of 0.81.

forest tree species classification entropy Sentinel-1/2 extreme gradient boosting

2025-09-22abs ↗pdf ↗

Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechiastat.ML

Study uses SAR data to estimate forest vegetation indices, improving monitoring of temperate forests.

problem Limitations of optical satellite data in monitoring forest ecosystems, especially due to atmospheric effects.

method Estimating four vegetation indices (LAI, FAPAR, EVI, NDVI) using multitemporal Sentinel-1 SAR and ancillary data.

result Accurate estimation of forest vegetation indices using SAR data, achieving high

R^2

and low MAE values.

SAR data forest monitoring vegetation indices machine learning Sentinel-1

2023-11-13abs ↗pdf ↗

From parcel to continental scale -- A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observationsstat.ML

First European crop map created using satellite data.

problem Need for detailed parcel-level crop type mapping for EU.

method Used Sentinel-1 radar observations and LUCAS in-situ data.

result 80.3% overall accuracy for 19 crop types, highest for rape and turnip rape.

crop mapping Sentinel-1 Copernicus random forest land use

2021-05-19abs ↗pdf ↗

Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationshipscs.LG

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.

land cover mapping Sentinel-1 Sentinel-2 multi-source multi-temporal

2019-11-20abs ↗pdf ↗

Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processeseess.IV

New method fuses optical and SAR data to fill LAI gaps during cloudy periods.

problem Cloudy periods mask key crop growth stages, leading to unreliable yield predictions.

method Multi-Output Gaussian Process (MOGP) regression for fusing Sentinel-1 RVI and Sentinel-2 LAI time series.

result MOGP provides improved LAI estimations even during cloudy periods, especially for long gaps.

cloudy periods crop growth yield predictions MOGP Sentinel-1

2020-12-05abs ↗pdf ↗

Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learningcs.CY

The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of wid…

2018-06-03abs ↗pdf ↗

Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentimentcs.LG

Study forecasts sub-city real estate prices weekly using radar and news sentiment.

problem Limited availability of reliable real estate price indicators at neighborhood and long horizons.

method Combining satellite radar signals and news sentiment to forecast sub-city real estate prices.

result The multimodal model reduces mean absolute error by 35% at long horizons (26-34 weeks).

real estate price forecasting sub-city satellite radar news sentiment

2026-02-20abs ↗pdf ↗

deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-lossstat.ML

Paper develops a deforestation detection system using optical and SAR data.

problem Detecting tree-loss in dense forests using satellite data.

method Combines optical and SAR data, uses KL expansion for anomaly detection, and Hidden Markov Model for classification.

result Hybrid method achieves high accuracy and robustness in sparse optical data.

deforestation satellite SAR optical Hidden Markov Model

2025-10-15abs ↗pdf ↗

Reconstruction of Sentinel-2 Time Series Using Robust Gaussian Mixture Models -- Application to the Detection of Anomalous Crop Development in wheat and rapeseed cropsstat.ML

Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.

problem Missing data in remote sensing images, especially from multispectral and SAR sensors.

method Robust Gaussian Mixture Models (GMM) with outlier detection using isolation forest.

result Robust GMM outperforms standard GMM in reconstructing imputed values, reducing errors.

Gaussian Mixture Models Remote Sensing Crop Monitoring Sentinel-2 Isolation Forest

2021-10-22abs ↗pdf ↗

Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan Francestat.ML

Study uses satellite and lidar data to map forest height and biomass in France.

problem Mapping forest resources and carbon in large areas.

method Machine learning approach using Sentinel-1, Sentinel-2, ALOS-2, and GEDI Lidar data.

result High-resolution maps of forest height and biomass produced with good accuracy.

forest mapping satellite imagery lidar carbon storage remote sensing

2023-10-23abs ↗pdf ↗