A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery

Accurate rooftop detection from historical aerial imagery is vital for examining long-term urban development and human settlement patterns. However, black-and-white analog photographs pose significant challenges for modern object detection frameworks due to their limited spatial resolution, lack of color information, and archival degradation. To address these limitations, this study introduces a two-stage image enhancement pipeline based on Generative Adversarial Networks (GANs): image colorization using DeOldify, followed by super-resolution enhancement with Real-ESRGAN. The enhanced images were then used to train and evaluate rooftop detection models, including Faster R-CNN, DETReg, and YOLOv11n. Results show that combining colorization with super-resolution substantially improves detection performance, with YOLOv11n achieving a mean Average Precision (mAP) exceeding 85%. This reflects an improvement of approximately 40% over original black-and-white images and 20% over images enhanced through colorization alone. The proposed method effectively bridges the gap between archival imagery and contemporary deep learning techniques, enabling more reliable extraction of building footprints from historical aerial photographs.

Translating multispectral imagery to nighttime imagery via conditional generative adversarial networks

Nighttime satellite imagery has been applied in a wide range of fields. However, our limited understanding of how observed light intensity is formed and whether it can be simulated greatly hinders its further application. This study explores the potential of conditional Generative Adversarial Networks (cGAN) in translating multispectral imagery to nighttime imagery. A popular cGAN framework, pix2pix, was adopted and modified to facilitate this translation using gridded training image pairs derived from Landsat 8 and Visible Infrared Imaging Radiometer Suite (VIIRS). The results of this study prove the possibility of multispectral-to-nighttime translation and further indicate that, with the additional social media data, the generated nighttime imagery can be very similar to the ground-truth imagery. This study fills the gap in understanding the composition of satellite observed nighttime light and provides new paradigms to solve the emerging problems in nighttime remote sensing fields, including nighttime series construction, light desaturation, and multi-sensor calibration.