Ding, XianzhongHong, WanshiAn, ZhiyuWang, BinDu, Wan2024-12-262024-12-262025-01-07978-0-9981331-8-87b396067-1a5c-4295-b53a-7ce9f0f82752https://hdl.handle.net/10125/109487Detecting parking lots is essential for siting electric truck charging infrastructure, ensuring convenient access for drivers. However, the private ownership of many parking lots limits public access to information for planners. Current object detection methods rely heavily on high-resolution satellite imagery, which is often restricted in availability. To address this, we introduce Deepot, a deep learning-based truck parking location identification approach using low-resolution satellite imagery. We begin by retrieving geographical data for the target area and optimizing image resolution for model training. The model produces initial parking lot locations, which are then converted to real global latitude and longitude coordinates using a custom coordinate reference systems transformer. These global coordinates facilitate querying Google Maps for high-resolution images to enhance detection performance. Deepot streamlines charging infrastructure planning from diverse satellite imagery and offers additional, high-fidelity candidate locations to medium- and heavy-duty infrastructure planning tool, HEVI-LOAD. Extensive experiments show the effectiveness of Deepot.10Attribution-NonCommercial-NoDerivatives 4.0 InternationalLocation Intelligence Research in System Sciencesdeep learning, electric truck charging infrastructure, low-resolution satellite imagery, object detection, parking lot detectionConference Paper10.24251/HICSS.2025.639