Trajectory modeling refers to characterizing human movement behavior, serving
as a pivotal step in understanding mobility patterns. Nevertheless, existing
studies typically ignore the confounding effects of geospatial context, leading
to the acquisition of spurious correlations and limited generalization
capabilities. To bridge this gap, we initially formulate a Structural Causal
Model (SCM) to decipher the trajectory representation learning process from a
causal perspective. Building upon the SCM, we further present a Trajectory
modeling framework (TrajCL) based on Causal Learning, which leverages the
backdoor adjustment theory as an intervention tool to eliminate the spurious
correlations between geospatial context and trajectories. Extensive experiments
on two real-world datasets verify that TrajCL markedly enhances performance in
trajectory classification tasks while showcasing superior generalization and
interpretability.

运用因果学习的轨迹建模框架（TrajCL）通过结构性因果模型（SCM）并基于干预工具（逆向调整理论）消除地理空间和轨迹之间的虚假相关性，提升轨迹分类任务的性能和泛化能力。

迈向稳健轨迹表示：用因果学习分离环境混淆因素

Towards Robust Trajectory Representations: Isolating Environmental  Confounders with Causal Learning

Modern cameras are equipped with a wide array of sensors that enable
recording the geospatial context of an image. Taking advantage of this, we
explore depth estimation under the assumption that the camera is geocalibrated,
a problem we refer to as geo-enabled depth estimation. Our key insight is that
if capture location is known, the corresponding overhead viewpoint offers a
valuable resource for understanding the scale of the scene. We propose an
end-to-end architecture for depth estimation that uses geospatial context to
infer a synthetic ground-level depth map from a co-located overhead image, then
fuses it inside of an encoder/decoder style segmentation network. To support
evaluation of our methods, we extend a recently released dataset with overhead
imagery and corresponding height maps. Results demonstrate that integrating
geospatial context significantly reduces error compared to baselines, both at
close ranges and when evaluating at much larger distances than existing
benchmarks consider.

利用现代相机所配备的各种传感器记录图像的地理空间环境，将在已知摄像机拍摄地点的情况下，提出一种基于地理启用的方法，使用一个识别出的综合地面深度地图与相对应的俯瞰图像，将其融合在编码器 / 解码器风格的分割网络内，实现对深度估计的目的。结果表明，将地理上下文集成到深度估计模型中可显着降低误差。