MAPPING POVERTY IN THE PHILIPPINES USING MACHINE LEARNING, SATELLITE IMAGERY, AND CROWD-SOURCED GEOSPATIAL INFORMATION

24 citations as recorded by crossref.

1. Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda F. Nkurunziza et al. 10.1080/23322039.2024.2444374
2. Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study J. Levy et al. 10.3389/fpubh.2021.766707
3. Exploring RAU-net for semantic segmentation of Philippines satellite images in identification of building density J. Oñate & M. Ang-Tolentino 10.1080/01431161.2021.1986239
4. A review of explainable AI in the satellite data, deep machine learning, and human poverty domain O. Hall et al. 10.1016/j.patter.2022.100600
5. Regional Wealth Data Acquisition and Modeling: Innovations Needed for Advancement in Sustainable Wealth in Energy-Rich Regions J. Hubbart & H. Ghadimi 10.3390/challe14040051
6. Mapping socioeconomic indicators using social media advertising data M. Fatehkia et al. 10.1140/epjds/s13688-020-00235-w
7. Big data and data science in global governance: anticipating future needs and applications in the UN and beyond L. Li et al. 10.3389/fpos.2025.1583772
8. Using satellite data and deep learning to estimate educational outcomes in data-sparse environments D. Runfola et al. 10.1080/2150704X.2021.1987575
9. Predicting Poverty Using Geospatial Data in Thailand N. Puttanapong et al. 10.3390/ijgi11050293
10. Using satellite imagery to understand and promote sustainable development M. Burke et al. 10.1126/science.abe8628
11. High-resolution comprehensive regional development mapping using multisource geographic data L. Li et al. 10.1016/j.scs.2024.105670
12. Using spatial video and deep learning for automated mapping of ground-level context in relief camps J. Ajayakumar et al. 10.1186/s12942-024-00382-7
13. Scalable big earth observation data mining algorithms: a review N. Sisodiya et al. 10.1007/s12145-023-01032-5
14. Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data S. Piaggesi et al. 10.3389/fdata.2022.1006352
15. A data-driven approach to mapping multidimensional poverty at residential block level in Mexico M. Zea-Ortiz et al. 10.1007/s10668-024-05230-z
16. Incidents1M: a Large-Scale Dataset of Images With Natural Disasters, Damage, and Incidents E. Weber et al. 10.1109/TPAMI.2022.3191996
17. Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania N. Bujiku Sende et al. 10.1109/ACCESS.2025.3537807
18. Exploring machine learning trends in poverty mapping: A review and meta-analysis B. Lamichhane et al. 10.1016/j.srs.2025.100200
19. Fine-Grained Large-Scale Vulnerable Communities Mapping via Satellite Imagery and Population Census Using Deep Learning J. Salas et al. 10.3390/rs13183603
20. THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT V. SEKARA et al. 10.1142/S0219525924400022
21. Non-traditional data sources I. Weber et al. 10.1145/3447739
22. A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance D. Runfola et al. 10.1080/13658816.2024.2305636
23. Exploring the use of satellite imagery and computer vision‐based machine learning method to improve the spatial granularity of poverty statistics M. Hofer et al. 10.1111/asej.12349
24. Deep learning and forecasting in practice: an alternative costs case T. Zema et al. 10.1016/j.procs.2022.09.354