The detrimental impacts of climate change include stronger and more destructive hurricanes happening all over the world. Identifying different damaged structures of an area including buildings and roads are vital since it helps the rescue team to plan their efforts to minimize the damage by a natural disaster. Semantic segmentation helps to identify different parts of an image. We implement a novel self-attention based semantic segmentation model on a high resolution UAV dataset and attain Mean IoU score of around88%on the test set. The result inspires to use self-attention schemes in natural disaster damage assessment which will save human lives and reduce economic losses.

翻译：气候变化的有害影响包括世界各地发生的更强烈、更具有破坏性的飓风。 查明一个地区不同的受损结构,包括建筑物和道路,至关重要,因为这有助于救援小组规划尽量减少自然灾害破坏的努力。 语义分割有助于识别图像的不同部分。 我们在无人机高分辨率数据集上实施了基于自我关注的新式语义分割模式,并在测试集中达到88%左右的惯性IOU分数。 其结果激励人们在自然灾害损害评估中使用自我注意计划,这将拯救人的生命,减少经济损失。