A Pipeline and NIR-Enhanced Dataset for Parking Lot Segmentation

Discussions of minimum parking requirement policies often include maps of parking lots, which are time-consuming to construct manually. Open-source datasets for such parking lots are scarce, particularly for US cities. This paper introduces the idea of using Near-Infrared (NIR) channels as input and several post-processing techniques to improve the prediction of off-street surface parking lots using satellite imagery. We constructed two datasets with 12,617 image-mask pairs each: one with 3-channel (RGB) and another with 4-channel (RGB + NIR). The datasets were used to train five deep learning models (OneFormer, Mask2Former, SegFormer, DeepLab V3, and FCN) for semantic segmentation, classifying images to differentiate between parking and non-parking pixels. Our results demonstrate that the NIR channel improved accuracy because parking lots are often surrounded by grass-even though the NIR channel needed to be upsampled from a lower resolution. Post-processing including eliminating erroneous 'holes,' simplifying edges, and removing road and building footprints further improved the accuracy. Best model, OneFormer trained on 4-channel input and paired with post-processing techniques achieves a mean Intersection over Union (mIoU) of 84.9% and a pixel-wise accuracy of 96.3%.