# Artificial Intelligence and Human Geography

arXiv: https://arxiv.org/abs/2312.08827 · HTML: https://mlsanity.com/paper/2312.08827/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020)
- [Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages](https://mlsanity.com/paper/2310.09340/) (2023)
- [Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability](https://mlsanity.com/paper/2012.14349/) (2020)
- [Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases](https://mlsanity.com/paper/2103.02155/) (2021)
- [Extracting human emotions at different places based on facial expressions and spatial clustering analysis](https://mlsanity.com/paper/1905.01817/) (2019)
- [Constructing Gazetteers from Volunteered Big Geo-Data Based on Hadoop](https://mlsanity.com/paper/1311.7676/) (2013)

## Abstract (arXiv)

This paper examines the recent advances and applications of AI in human geography especially the use of machine (deep) learning, including place representation and modeling, spatial analysis and predictive mapping, and urban planning and design. AI technologies have enabled deeper insights into complex human-environment interactions, contributing to more effective scientific exploration, understanding of social dynamics, and spatial decision-making. Furthermore, human geography offers crucial contributions to AI, particularly in context-aware model development, human-centered design, biases and ethical considerations, and data privacy. The synergy beween AI and human geography is essential for addressing global challenges like disaster resilience, poverty, and equitable resource access. This interdisciplinary collaboration between AI and geography will help advance the development of GeoAI and promise a better and sustainable world for all.