unicef-ai4d-poverty-mapping
Poverty mapping
Develops datasets and machine learning models to estimate poverty levels across Southeast Asia using geospatial analysis and machine learning techniques.
UNICEF AI4D Relative Wealth Mapping Project - datasets, models, and scripts for building relative wealth estimation models across Southeast Asia (SEA)
21 stars
3 watching
9 forks
Language: Jupyter Notebook
last commit: 9 months ago
Linked from 1 awesome list
geospatial-analysismachine-learningsocial-impact
Related projects:
Repository | Description | Stars |
---|---|---|
| An effort to analyze socioeconomic data using satellite imagery and machine learning techniques. | 43 |
| Curated resources and code projects for machine learning in geospatial data science | 564 |
| Training and deploying large language models on computer vision tasks using region-of-interest inputs | 517 |
| Developing deep learning models for Earth Observation using remote sensing images | 477 |
| Maps biodiversity from remote-sensed images using statistical methods | 49 |
| A deep learning library for image segmentation of cultivated land from satellite data | 27 |
| A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,236 |
| Automatically inferring 2D and 3D change detection maps from bitemporal optical images without relying on DSMs. | 29 |
| A large-scale dataset and software framework for remote sensing-based flood mapping using SAR data | 44 |
| A framework for building geospatial machine learning models that fuse multiple datasets to gain insights into agriculture and sustainability | 694 |
| Replicates a map showing regional population structures based on a 2018 paper by Kashnitsky and Schöley. | 80 |
| Develops a method to learn shared latent structure between biomedical images and gene expression data | 25 |
| A Java library providing a range of machine learning algorithms and tools for statistical analysis | 791 |
| Maps deforestation risk using a forest cover change map and the JNR methodology | 24 |
| A repository of statistical modeling materials and resources for an R course on Bayesian inference | 2,018 |