arXiv Machine Learning
Climate Variability Modulates the Impact of Price Spikes on Food Insecurity
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arXiv Machine Learning
Sep 22
Credit Access is Associated with Improved Food Security in the Horn of Africa
arXiv:2609.24382v1 Announce Type: new Abstract: The intensification of climate change poses a growing threat to food security, especially in vulnerable communities. This study employs an observationa...
arXiv Machine Learning
Sep 25
Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling
The paper introduces DeepX-GAN, a deep generative model that captures spatial dependence in rare climate extremes. It can simulate statistically plausible unseen heat extremes—both direct-hit and near-miss events—beyond the observed record. Applied to the Middle East and North Africa, the model shows that unseen heat extremes disproportionately affect vulnerable countries and that future warming could create new persistent hotspots, underscoring the need for spatially adaptive resilience planning.
Hugging Face Trending Papers
Sep 14
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these...
arXiv AI
Sep 15
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
The paper presents a transfer‑learning approach that adapts a multimodal spatiotemporal vision transformer, originally trained on Demographic and Health Survey data, to estimate socioeconomic conditions in forced‑displacement settings. Using satellite‑derived geospatial covariates, the adapted model explains up to 66% of variation in socioeconomic outcomes in camp‑intersecting grids and 41% in non‑camp areas, achieving mean absolute errors of 4.37 and 5.41 index points respectively. This framework supplements periodic household surveys by providing regularly updated, spatially granular socioeconomic estimates that bridge data gaps between survey rounds.
arXiv Machine Learning
Aug 20
Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling
The paper presents a modular, scalable framework for estimating the probability of failure (PoF) of power‑line assets using geospatial machine learning. It integrates diverse environmental predictors—topography, vegetation indices, lightning climatology, proximity features, and operational records—to model vegetation‑ and lightning‑related failure modes. The architecture is designed to be computationally efficient, easily extensible to new data sources, and suitable for large‑scale utility deployment, enabling asset‑level risk stratification for inspection and resilience planning.
arXiv Machine Learning
Sep 10
VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation
arXiv:2606.21961v2 Announce Type: replace Abstract: Vegetation monitoring under climate stress requires answering not only how it will evolve given the expected weather, but how it would respond to a...