This work addresses the task of modeling spatiotemporal traffic patterns
directly from overhead imagery, which we refer to as image-driven traffic
modeling. We extend this line of work and introduce a multi-modal, multi-task
transformer-based segmentation architecture that can be used to create dense
city-scale traffic models. Our approach includes a geo-temporal positional
encoding module for integrating geo-temporal context and a probabilistic
objective function for estimating traffic speeds that naturally models temporal
variations. We evaluate our method extensively using the Dynamic Traffic Speeds
(DTS) benchmark dataset and significantly improve the state-of-the-art.
Finally, we introduce the DTS++ dataset to support mobility-related location
adaptation experiments.

用遥感图像直接建模时空交通模式的多模态、多任务变压器分割架构，包含地理时空位置编码模块和自然建模时间变化的概率目标函数，在 Dynamic Traffic Speeds (DTS) 数据集上显著提升交通模型的性能，并引入 DTS++ 数据集支持与移动相关的位置适应实验。