# Dynamic Traffic Modeling From Overhead Imagery

arXiv: https://arxiv.org/abs/2012.10530 · HTML: https://mlsanity.com/paper/2012.10530/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images](https://mlsanity.com/paper/2307.15904/) (2023)
- [Revisiting Near/Remote Sensing with Geospatial Attention](https://mlsanity.com/paper/2204.01807/) (2022)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [Handling Image and Label Resolution Mismatch in Remote Sensing](https://mlsanity.com/paper/2211.15790/) (2022)
- [Active Geospatial Search for Efficient Tenant Eviction Outreach](https://mlsanity.com/paper/2412.17854/) (2024)
- [Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services](https://mlsanity.com/paper/2308.06515/) (2023)
- [Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery](https://mlsanity.com/paper/2209.02564/) (2022)
- [Probabilistic Image-Driven Traffic Modeling via Remote Sensing](https://mlsanity.com/paper/2403.05521/) (2024)

## Abstract (arXiv)

Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A traditional approach for solving this problem would be to model the speed of each road segment as a function of time. However, this strategy is limited in that a significant amount of data must first be collected before a model can be used and it fails to generalize to new areas. Instead, we propose an automatic approach for generating dynamic maps of traffic speeds using convolutional neural networks. Our method operates on overhead imagery, is conditioned on location and time, and outputs a local motion model that captures likely directions of travel and corresponding travel speeds. To train our model, we take advantage of historical traffic data collected from New York City. Experimental results demonstrate that our method can be applied to generate accurate city-scale traffic models.