# Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Data

arXiv: https://arxiv.org/abs/2506.22499 · HTML: https://mlsanity.com/paper/2506.22499/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 91 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery](https://mlsanity.com/paper/1805.09512/) (2018)
- [Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery](https://mlsanity.com/paper/2411.02136/) (2024)
- [Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs](https://mlsanity.com/paper/1903.04681/) (2019)
- [Real-time system optimal traffic routing under uncertainties -- Can physics models boost reinforcement learning?](https://mlsanity.com/paper/2407.07364/) (2024)
- [Truck Traffic Monitoring with Satellite Images](https://mlsanity.com/paper/1907.07660/) (2019)
- [Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data](https://mlsanity.com/paper/2501.03492/) (2025)
- [Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory Data](https://mlsanity.com/paper/2411.05842/) (2024)
- [Compressive Origin-Destination Matrix Estimation](https://mlsanity.com/paper/1404.3263/) (2014)
- [Multiday User Equilibrium with Strategic Commuters](https://mlsanity.com/paper/2212.12583/) (2022)
- [Predicting Regional Road Transport Emissions From Satellite Imagery](https://mlsanity.com/paper/2312.10551/) (2023)
- [Traffic estimation in unobserved network locations using data-driven macroscopic models](https://mlsanity.com/paper/2401.17095/) (2024)
- [A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring](https://mlsanity.com/paper/2501.09249/) (2025)

## Abstract (arXiv)

This study presents a novel integrated framework for dynamic origin-destination demand estimation (DODE) in multi-class mesoscopic network models, incorporating high-resolution satellite imagery together with conventional traffic data from local sensors. Unlike sparse local detectors, satellite imagery offers consistent, city-wide road and traffic information of both parking and moving vehicles, overcoming data availability limitations. To extract information from imagery data, we design a computer vision pipeline for class-specific vehicle detection and map matching, generating link-level traffic density observations by vehicle class. Building upon this information, we formulate a computational graph-based DODE framework that calibrates dynamic network states by jointly matching observed traffic counts/speeds from local sensors with density measurements derived from satellite imagery. To assess the accuracy and robustness of the proposed framework, we conduct a series of numerical experiments using both synthetic and real-world data. The results demonstrate that supplementing traditional data with satellite-derived density significantly improves estimation performance, especially for links without local sensors. Real-world experiments also show the framework's potential for practical deployment on large-scale networks. Sensitivity analysis further evaluates the impact of data quality related to satellite imagery data.