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...
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.
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these...
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.
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: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...
The paper introduces ESIA, an Earth Surface Immune System that detects and recognizes unknown anomalies in satellite imagery without prior category knowledge. It uses a non‑specific innate stage for rapid localization and a specific adaptive stage that matches image patches to text prompts via a multi‑modal model, achieving high F1 scores. The system adapts to new scenes in seconds and has been validated on a large global dataset, with applications to farmland degradation after the Kakhovka Dam collapse and burn severity assessment from the 2025 Palisades Fire.
This study examined whether food retail and street‑market indicators differentiate social vulnerability levels across 76 districts of São Paulo. Using machine‑learning classifiers on district‑level data, the authors found that densities of healthy and unhealthy food establishments explained about 60 % of feature importance, with XGBoost achieving the highest mean F‑score (0.75). The results suggest that publicly available food‑environment data are linked to district‑level social vulnerability, though limitations such as small sample size and cross‑sectional design restrict causal inference.
arXiv:2608. 09966v1 Announce Type: cross Abstract: European summer warming reflects interactions among background change, persistent ocean--land--circulation states, and same-season variability.
The Planetary Prediction Engine (PPE) is an autonomous AI system that transforms natural-language queries into end-to-end geospatial predictions. It automatically retrieves and fuses multimodal datasets from open-web and Earth observation sources, incorporates foundation model embeddings, and searches task‑specific model families with overfitting safeguards. Across multiple domains, PPE outperforms state‑of‑the‑art baselines, improving regression metrics for CDC health indicators, FEMA risk indices, and the Social Vulnerability Index, doubling accuracy for Nigerian food security indicators, and achieving higher recall in Ebola outbreak nowcasting.
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.
arXiv:2608.24665v1 Announce Type: new Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whe...