Quantifying Post-Earthquake Urban Green Space Loss Using an Optimized Deep Learning-Based Semantic Segmentation Framework

Abstract

Large-scale earthquakes and the rapid reconstruction processes that follow lead to significant spatial transformations in urban ecosystems, particularly exerting pronounced impacts on intra-urban green spaces. This study quantitatively evaluates changes in urban green areas that occurred after the 2023 Kahramanmaraş earthquakes in Hatay (Antakya) and Elazığ (Güneykent) by utilizing high-resolution satellite imagery and an optimized deep learning–based semantic segmentation approach. In the proposed framework, an enhanced U-Net architecture incorporating asymmetric and dilated convolutional layers was designed to improve parameter efficiency while preserving extensive contextual information. The proposed model, comprising approximately 1.7 million parameters, achieved 88% accuracy, 0.76 Intersection over Union (IoU), and a 0.86 Dice score, demonstrating competitive performance compared to contemporary segmentation models with substantially higher parameter counts. Pixel-level comparison of pre- and post-earthquake imagery revealed reductions in green space reaching up to 89% in the examined sub-regions, with even higher rates observed in certain localized areas. The findings indicate that intensive post-disaster construction and urban reorganization processes may impose substantial pressure on environmental sustainability. The proposed method provides a computationally efficient and scalable decision-support framework for monitoring post-disaster environmental changes and developing sustainable urban planning strategies.