GreenNet: AI Maps Urban Green Like Magic! #ScienceFather #researchawards

Urban green spaces provide a range of essential ecological benefits, including reducing noise, purifying air, cooling urban environments, and improving public health. However, accurately identifying and assessing these spaces is challenging due to their varied and complex distribution across large areas. Traditional field surveys are time-consuming and limited in scope, while remote sensing offers a more scalable and efficient solution. Yet, extracting meaningful information from high-resolution satellite images requires advanced data processing techniques, especially in densely built and visually complex urban environments.

Recent advancements in deep learning, particularly convolutional neural networks (CNNs) and transformer-based models, have significantly improved the ability to analyze satellite imagery. CNN-based models like U-Net and SegNet are widely used for image segmentation, but they struggle to capture long-range dependencies. On the other hand, transformers can model global relationships effectively but face limitations with fine spatial details and high computational costs. To address these shortcomings, hybrid models combining CNNs and transformers have been developed to improve both accuracy and efficiency in classifying urban green spaces.

To overcome the limitations of existing methods, GreenNet is proposed as a novel dual-encoder architecture for urban green space classification using high-resolution remote sensing images. It includes an inside encoder for capturing intra-image features and an outside encoder for modeling inter-image dependencies. These are fused in the decoder using a transformer-based module called OGLAB, which enhances the network's ability to handle both local details and large-scale context. Additionally, boundary loss is computed using edge maps from the Segment Anything Model to improve boundary precision. GreenNet demonstrates strong performance and offers a promising solution for effective green space classification in complex urban settings.

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