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.
Satellite imagery helps assess sustainable development with machine learning.
Study uses cGAN to translate multispectral to nighttime satellite imagery.
Unsupervised method removes satellite noise without paired data.
Satellite imagery improves house price prediction models.
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to …
Deep learning models perform variably across continents/seasons in land cover mapping.
Satellite imagery and ML improve livelihood measurements and estimate electrification's impact.
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classi…
Study uses aerial or satellite imagery to improve land price prediction in Thailand.
HighRes-net enhances satellite imagery by fusing multiple low-res views.
Satellite imagery helps adjust for unobserved confounders in observational studies.
A cased-based reasoning method predicts rare events on strategic sites using satellite imagery.
Roads are critically important infrastructure to societal and economic development, with huge investments made by governments every year. However, methods for monitoring those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. In this work, we develop…
In this paper, the authors aim to combine the latest state of the art models in image recognition with the best publicly available satellite images to create a system for landslide risk mitigation. We focus first on landslide detection and further propose a similar system to be used for prediction. Such models are valu…
Study uses remotely sensed data to infer economic outcomes in experiments and quasi-experiments.
In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite image…
FedSpace optimizes ML training on satellites and ground stations.
Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both gove…
New methods detect roads in low-res satellite data, overcoming visibility challenges.
High-resolution satellite imagery have been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Even though, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in …
New method detects TC imagery patterns for rapid intensity change.
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine lea…
This paper presents a useful method to achieve classification in satellite imagery. The approach is based on pixel level study employing various features such as correlation, homogeneity, energy and contrast. In this study gray-scale images are used for training the classification model. For supervised classification, …
The paper classifies U.S. crop types using hyperspectral satellite imagery.
Procedural terrain generation for video games has been traditionally been done with smartly designed but handcrafted algorithms that generate heightmaps. We propose a first step toward the learning and synthesis of these using recent advances in deep generative modelling with openly available satellite imagery from NAS…
Mapping the spatial distribution of poverty in developing countries remains an important and costly challenge. These "poverty maps" are key inputs for poverty targeting, public goods provision, political accountability, and impact evaluation, that are all the more important given the geographic dispersion of the remain…
Improved satellite image captions enhance descriptiveness without large models.
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…
Method uses aggregate crop statistics to improve satellite-based crop type mapping.
Satellite images predict U.S. county mortality rates.
Generative adversarial approach for satellite image time series land cover classification.
Paper proposes decision-theoretic approach to combat wildfires.
New method extracts features from large datasets using transport operators.
Study uses satellite and lidar data to map forest height and biomass in France.
Cross-prediction improves inference from small labeled datasets.
Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both gove…
Expanding self-supervised learning to diverse domains reveals Rotation's semantic superiority.
CNNs improve InSAR image denoising and coherence estimation.
Paper introduces multi-scale methods to improve CATE estimation from EO data.
EcoCast predicts biodiversity risks using satellite data and citizen science records.
A new data set helps estimate continental-scale population distributions.
Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, d…
AI helps forecasters understand TC convective evolution before intensification.
Dimensionality reduction is a topic of recent interest. In this paper, we present the classification constrained dimensionality reduction (CCDR) algorithm to account for label information. The algorithm can account for multiple classes as well as the semi-supervised setting. We present an out-of-sample expressions for …
Provides a compendium of data sources for various applications.
New methods reduce bias in machine learning predictions for causal inference without extra data.
More than one billion people live in slums around the world. In some developing countries, slum residents make up for more than half of the population and lack reliable sanitation services, clean water, electricity, other basic services. Thus, slum rehabilitation and improvement is an important global challenge, and a …
Deep Learning is gaining traction with geophysics community to understand subsurface structures, such as fault detection or salt body in seismic data. This study describes using deep learning method for iceberg or ship recognition with synthetic aperture radar (SAR) data. Drifting icebergs pose a potential threat to ac…