KAN-Attn GAN: Map Generation with Kolmogorov-Arnold Networks and Attention-Based Queries Selection

Dynamic urban development and natural disasters often lead to discrepancies between maps and satellite images, posing challenges for autonomous navigation and smart city planning. Automatic map generation from satellite imagery is crucial for addressing these discrepancies and ensuring accurate geospatial data. While Generative Adversarial Networks (GANs) favored with contrastive learning have been used for this task, limitations such as randomness in feature selection result in suboptimal performance, and MLP-based projection heads lack the expressiveness needed for effective feature generation. To address these limitations, we propose a novel model, KAN-Attn, which incorporates an attention-based query selection mechanism for selecting relevant features in contrastive learning. Also, KAN-Attn is the first application of Kolmogorov-Arnold Network (KAN) to improve feature generation with enhanced expressiveness in map generation. Our innovations allow KAN-Attn to achieve optimal performance in map generation, with experiments on publicly available datasets demonstrating state-of-the-art results based on relevant metrics on perceptual quality.