{"total":25,"rows":[{"id":"2310.06213","title":"GeoLLM: Extracting Geospatial Knowledge from Large Language Models","created":"2023-10-10","cited_by_count":64,"area":null},{"id":"2109.02890","title":"Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access","created":"2021-09-07","cited_by_count":13,"area":null},{"id":"2303.18087","title":"Evaluation Challenges for Geospatial ML","created":"2023-03-31","cited_by_count":10,"area":null},{"id":"2408.12133","title":"Self-Supervised Representation Learning for Geospatial Objects: A Survey","created":"2024-08-22","cited_by_count":9,"area":null},{"id":"2305.01783","title":"Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy","created":"2023-05-02","cited_by_count":7,"area":null},{"id":"2411.14354","title":"Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas","created":"2024-11-21","cited_by_count":6,"area":null},{"id":"2308.08656","title":"Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data","created":"2023-08-16","cited_by_count":5,"area":null},{"id":"2405.20868","title":"Responsible AI for Earth Observation","created":"2024-05-31","cited_by_count":4,"area":null},{"id":"2505.08662","title":"Revealing economic facts: LLMs know more than they say","created":"2025-05-13","cited_by_count":3,"area":null},{"id":"2508.01341","title":"Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: \"One Map, Many Trials\" in Satellite-Driven Poverty Analysis","created":"2025-08-02","cited_by_count":2,"area":null},{"id":"2604.23166","title":"A satellite foundation model for improved wealth monitoring","created":"2026-04-25","cited_by_count":2,"area":null},{"id":"2206.11400","title":"Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan","created":"2022-06-22","cited_by_count":1,"area":null},{"id":"2211.08735","title":"Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?","created":"2022-11-16","cited_by_count":1,"area":null},{"id":"2302.10793","title":"Interpreting wealth distribution via poverty map inference using multimodal data","created":"2023-02-17","cited_by_count":1,"area":null},{"id":"2310.00233","title":"CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images","created":"2023-09-30","cited_by_count":1,"area":null},{"id":"2411.02855","title":"Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach","created":"2024-11-05","cited_by_count":1,"area":null},{"id":"2107.14700","title":"Seeing poverty from space, how much can it be tuned?","created":"2021-07-30","cited_by_count":0,"area":null},{"id":"2307.11921","title":"Poverty rate prediction using multi-modal survey and earth observation data","created":"2023-07-21","cited_by_count":0,"area":null},{"id":"2408.01631","title":"A Comparative Analysis of Wealth Index Predictions in Africa between three Multi-Source Inference Models","created":"2024-08-03","cited_by_count":0,"area":null},{"id":"2411.06436","title":"Predictors of disease outbreaks at continentalscale in the African region: Insights and predictions with geospatial artificial intelligence using earth observations and routine disease surveillance data","created":"2024-11-10","cited_by_count":0,"area":null},{"id":"2411.08934","title":"Predicting household socioeconomic position in Mozambique using satellite and household imagery","created":"2024-11-13","cited_by_count":0,"area":null},{"id":"2607.08281","title":"Enhancing the KidSat Model: Integrating Geographical Encoding and Data Quality Assessment for Childhood Poverty Prediction","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2608.07871","title":"Crowd-Sourced Geographies of Income: Using Google Maps Points of Interest as High-Frequency Proxies for Sub-Municipal Income Estimation in Sao Paulo, Brazil","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2608.23322","title":"Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.15773","title":"Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings","created":"2026-09-14","cited_by_count":0,"area":null}],"next_cursor":null}