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.
Study uses remotely sensed data to infer economic outcomes in experiments and quasi-experiments.
Securely trains neural networks remotely with deep learning's flaws.
This paper improves land cover classification using global spatial features in CNN.
Method matches noisy remote sensing images robustly.
Paper proposes an active learning method to improve remote sensing object detection with less labeled data.
The concept of explainability is envisioned to satisfy society's demands for transparency on machine learning decisions. The concept is simple: like humans, algorithms should explain the rationale behind their decisions so that their fairness can be assessed. While this approach is promising in a local context (e.g. to…
Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success. Since generating sensor speci…
Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal conv…
Researchers predict butt rot volume using harvester data and remote sensing.
Estimates Mozambique's population using remote sensing and microcensus data.
We present the OpenAI Remote Rendering Backend (ORRB), a system that allows fast and customizable rendering of robotics environments. It is based on the Unity3d game engine and interfaces with the MuJoCo physics simulation library. ORRB was designed with visual domain randomization in mind. It is optimized for cloud de…
Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant. However, in some instances, such as change detection in remote sensing images, annotated data cannot be obtained in sufficient quantities. In…
Paper introduces kernel methods for detecting anomalous changes in remote sensing imagery.
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significa…
The paper proposes a method to create efficient remote monitoring models.
Researchers develop multi-agent systems for quadcopters to collaborate in missions.
We present the remote stochastic gradient (RSG) method, which computes the gradients at configurable remote observation points, in order to improve the convergence rate and suppress gradient noise at the same time for different curvatures. RSG is further combined with adaptive methods to construct ARSG for acceleration…
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the po…
Improved detection of burnt areas in satellite images using evolved hyper-features.
Bayesian optimization improves forest inventory sampling using remote sensing data.
Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples. In practice, collecting large scale accurately labeled datasets is a chal…
In this paper, some patterns of the Neuron Response of deep Convolutional Neural Networks were observed.
Study shows MDA's effectiveness even when more components are assumed than in actual data.
Geospatial ML models need special evaluation methods due to their unique challenges.
Although a key driver of Earth's climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate net radiation, latent and sensible heat and their uncer…
CNNs improve InSAR coherence classification.
CNNs improve InSAR image denoising and coherence estimation.
A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or pri…
SMAPGAN generates styled map tiles from remote sensing images.
New method uses deep neural networks to interpolate spatiotemporal data.
Paper develops a method to create accurate emulators of expensive computer codes.
Paper discusses new stochastic algorithms for sparse signal recovery.
Gaussian Processes improve geoscience data analysis.
Current remote sensing image classification problems have to deal with an unprecedented amount of heterogeneous and complex data sources. Upcoming missions will soon provide large data streams that will make land cover/use classification difficult. Machine learning classifiers can help at this, and many methods are cur…
Optimizes antenna tilt for better QoS in cellular networks.
Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
Deep learning usually requires big data, with respect to both volume and variety. However, most remote sensing applications only have limited training data, of which a small subset is labeled. Herein, we review three state-of-the-art approaches in deep learning to combat this challenge. The first topic is transfer lear…
Paper generates natural adversarial examples for hyperspectral data.
Deep learning applied to SAR data is explored in this paper.
Bayesian method improves segmentation accuracy with noisy labels.
PARyOpt is a python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive for computational optimization due to its low cost function footprint as well as the ability to account for uncertainties in data. A key…
BPR matches NN accuracy in crop classification while being more transparent.
Study improves paddy rice yield predictions in Peru using sparse regression and climatic variables.
Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods. The vast majority of remote sensing approaches either selectively choose single cloud-free observations or employ a pre-classification strategy to identify and mask cloudy pixels. We follow a different st…
Recently, a variety of approaches has been enriching the field of Remote Sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited faced to the rich spatio-spectral content of today's large datasets. It would seem intriguing to resort to Deep Learning (DL) based approaches at this stage…
Study uses cGAN to translate multispectral to nighttime satellite imagery.
Gaussian processes enhance EO with accurate and uncertain predictions.
Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning…