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In order to address the challenges of inaccurate region segmentation and insufficient detail restoration in flood disaster recognition models within complex urban environments, AttResU-Net, an enhanced U-Net semantic segmentation model integrating residual networks and a self-attention mechanism was proposed. Building upon the classical U-Net architecture, the model employed a deep residual network as the encoder to strengthen feature representation. Simultaneously, self-attention mechanisms were incorporated into the decoder to enhance response capability on key flood-related regions. A comprehensive training and testing pipeline was established. The improved AttResU-Net was trained and evaluated on the FloodNet dataset, which contains diverse and complex urban environmental categories. Quantitative metrics and qualitative visual results demonstrate the model's superior performance, achieving a mean pixel accuracy (mPA) of 79.75%, pixel accuracy (PA) of 90.01%, and mean precision (mPrecision) of 81.78%. Comparative experiments against state-of-the-art models reveal that AttResU-Net attains significantly higher segmentation accuracy and global recognition capability, particularly for urban features such as trees, water bodies, roads, and buildings.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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