# Smart City Development with Urban Transfer Learning

arXiv: https://arxiv.org/abs/1808.01552 · HTML: https://mlsanity.com/paper/1808.01552/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2018.

## Built on by (most cited first, 4 of 4)

- [UrbanCross: Enhancing Satellite Image-Text Retrieval with Cross-Domain Adaptation](https://mlsanity.com/paper/2404.14241/) (2024)
- [Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems](https://mlsanity.com/paper/2112.00424/) (2021)
- [Traffic Prediction with Transfer Learning: A Mutual Information-based Approach](https://mlsanity.com/paper/2303.07184/) (2023)
- [SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City](https://mlsanity.com/paper/2110.09452/) (2021)

## Abstract (arXiv)

Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with limited data?'. To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city planners and relevant practitioners with guidelines on how to apply this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies in public safety, transportation management, etc. We also summarize a few research opportunities and expect this article can attract more researchers to study urban transfer learning.