# GeoAI in Social Science

arXiv: https://arxiv.org/abs/2401.05398 · HTML: https://mlsanity.com/paper/2401.05398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps](https://mlsanity.com/paper/1707.03553/) (2017)
- [Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation](https://mlsanity.com/paper/1806.06671/) (2018)
- [Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection](https://mlsanity.com/paper/2103.04009/) (2021)

## Abstract (arXiv)

GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence. This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data and knowledge gaps. It also discusses the importance of breaking down data silos, accelerating convergence among GeoAI research methods, as well as moving GeoAI beyond geospatial benefits.