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.
By Steven Ndung'u, Adel Daoud, Ismael Yacoubou Djima, Hai-Anh H. Dang, Patrick Michael Brock
The paper presents an uncertainty‑aware machine‑learning approach for mapping poverty in Africa using satellite imagery. By combining simultaneous quantile regression with a novel conformal prediction technique, the authors generate statistically guaranteed prediction intervals for neighborhood‑level International Wealth Index estimates, achieving high explanatory power (R² = 0.75) while acknowledging broader uncertainty. They also propose a risk‑controlled aid allocation procedure that leverages both survey data and model predictions, showing in simulations that it can deliver more aid per eligible recipient than alternative strategies.
By Markus B. Pettersson, James Bailie, Mohammad Kakooei, Eagon Meng, Adel Daoud
DeepC4 is a deep learning-based spatial disaggregation method that uses local census statistics as cluster-level constraints and incorporates multiple conditional label relationships in a multitask learning framework. Applied to Rwandan urban morphology, it achieves macro‑F1 scores of 0.63, 0.78, and 0.45 for roof, wall, and height prediction, respectively, and estimates national dwelling and occupant counts within about 1.1% error compared to census records. The approach outperforms existing GEM and METEOR methods and covers 32‑49% more 500‑meter grid pixels across provinces.
By Joshua Dimasaka, Christian Gei{\ss}, Emily So
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...
By Jordi Cerd\`a-Bautista, Vasileios Sitokonstantinou, Jos\'e Manuel Veiga L\'opez-Pe\~na, Duccio Piovani, Jos\'e Mar\'ia T\'arraga, Gustau Camps-Valls
arXiv:2607. 03298v1 Announce Type: cross Abstract: Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change.
By Carlos Rodriguez-Pardo, Massimo Tavoni
The study evaluates whether geospatial foundation models derived from 2022 satellite data can capture physical aspects of place that conventional area-based social risk indices miss. Using LightGBM on data from 82,646 census tracts, the models moderately predicted certain survey variables and explained up to 54% of the residual variance in 40 health outcomes, notably improving predictions for annual checkups, arthritis, and high blood pressure. The models’ explanatory power increased with larger tract sizes, suggesting they add valuable, health-relevant information beyond traditional social risk measures.
By Nathaniel Hendrix, Carl Y. Zhang, Chris Heitzig, Andrew Bazemore, David H. Rehkopf
arXiv:2608. 00012v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated.
By Fengxiang Wang, Qiuyang Yu, Yueying Li, Mingshuo Chen, Chengchi Fei, Kaiyi Xu, Lixin Gu, Wangxu Wei, Junchao Gong, Lipeng Ma, Jiong Wang, Fenghua Ling, Wenlong Zhang, Xue Yang, Wenjing Yang, Ben Fei, Long Lan
arXiv:2608. 07871v1 Announce Type: cross Abstract: Accurate, up-to-date income data at the sub-municipal scale is essential for social policy in middle-income countries, yet in Brazil it depends on a costly decennial census whose intercensal gap recently exceeded a decade.
By Adrienne C. Kinney, Anya Workman, Ademar Takeo Akabane, Jenna Barac, Paulo Fernando Braga Carvalho, Jeova Farias, Fernando Nascimento, Paulo Ricardo da Silva Oliveira
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.
By Evelyn Ma, Rama Kumar Pasumarthi, Kishwar Shafin, Mandar Sharma, Mimi Sun, Hamed Sadeghi, Dav M. Ebengo, Mbulayi Onesime, Rouslan Solomakhin, John Wamburu, William Ogallo, Aisha Walcott-Bryant, Sanxing Chen, Arbaaz Muslim, Yael Mayer, Ronald Ho, Roy Lee, Ruth Alcantara, Abdoulaye Diack, Monica Bharel, Lambert Rosique, Jeremy Amez-Droz, Christopher Haire, James Manyika, Yossi Matias, Niv Efron, Gautam Prasad, Shravya Shetty
arXiv:2607. 24180v1 Announce Type: cross Abstract: Monitoring post-disaster recovery is essential for understanding how urban systems rebuild and progressively return to functionality.
By Luigi Russo, Deodato Tapete, Silvia Liberata Ullo, Paolo Gamba
arXiv:2609.07982v1 Announce Type: new
Abstract: Standard semi-supervised learning (SSL) typically relies on labelled and unlabelled data sharing a common marginal distribution. This assumption is oft...
By Bright Wiredu Nuakoh, Francky Fouedjio, Stephen Bradshaw, Yaw Kwaafo Awuah-Mensah, Wei Hong Tan, Emet Arya, Ebenezer Afrifa-Yamoah
arXiv:2608. 12422v1 Announce Type: new Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress.
By Matthew Kahn, Milan Arjel, Nirmala Adhikari, Mingmar Sherpa, James Pope