The field of automated image analysis for construction and urban planning is rapidly advancing, with a focus on developing innovative methods for semantic segmentation, object detection, and image enhancement. Recent developments have highlighted the potential of deep learning techniques, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), for improving the accuracy and efficiency of image analysis tasks. Notable advancements include the use of self-supervised learning approaches, which can reduce the need for large amounts of labeled training data, and the development of efficient and lightweight models that can be applied to high-resolution images. These advancements have significant implications for a range of applications, including automated building construction, urban planning, and environmental monitoring. Some noteworthy papers in this area include:

• A study on semantic segmentation for building houses from wooden cubes, which achieved high accuracy using a U-Net based approach.
• A paper on efficient building roof type classification using a self-supervised approach with EfficientNet architectures, which achieved state-of-the-art performance with significantly fewer parameters.
• A study on rooftop detection from historical aerial imagery, which used a GAN-based image enhancement pipeline to improve detection accuracy.
• A paper on pan-sharpening via learnable look-up tables, which proposed a novel and efficient method for merging high-resolution panchromatic and low-resolution multispectral images.
• A study on self-supervised pretraining for aerial road extraction, which improved segmentation performance and reduced reliance on labeled data.
• A paper on SmartScan, an AI-based interactive framework for automated region extraction from satellite images, which leveraged a prompt-based transformer for zero-shot segmentation.