The rapid growth in freight transportation, particularly heavy-duty trucks, poses significant environmental and public health challenges for communities near major ports and freeways. In areas such as those near the Port of Los Angeles and the I-710 corridor, communities are exposed to elevated levels of air pollution, noise pollution, and associated health risks. Traditional traffic data collection methods primarily concentrate on gathering traffic volume data for freeway segments or smaller areas, often overlooking heavy-duty vehicles across roadway networks and in local communities.

To better understand the environmental impact and spatial distribution of heavy-duty truck traffic, we employed a deep learning approach to analyze satellite imagery and publicly accessible spatial data. This approach allowed us to identify and categorize heavy-duty trucks and shipping containers along critical freight routes and analyze impacts on adjacentcommunities.