用0.5米分辨率影像和深度学习检测古吉拉特邦城市树的变化
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning
- 用YOLOv7模型在6500张图像上训练，实现树木分割与检测
- 模型达到71.5%的树木检测mAP，优化后达80%准确率，误分割仅2%
- 适合城市规划、环境监测人员参考，可高精度追踪城市绿化变化
本研究利用印度城市地区0.5米高分辨率卫星数据，验证深度学习模型在艾哈迈达巴德市的应用效果。基于6500张图像的精细化树冠数据集，训练了YOLOv7实例分割模型以实现变化检测。训练过程中采用边界框回归、掩码回归损失、平均精度（mAP）及随机梯度下降算法评估与优化模型性能。经过500轮训练，个体树木检测与树冠掩码分割的mAP分别达到0.715和0.699。通过进一步调优超参数，最高实现80%的树木检测准确率，且误分割率仅为2%。
原文摘要 · Abstract (English)
In this study, 0.5m high resolution satellite datasets over Indian urban region was used to demonstrate the applicability of deep learning models over Ahmedabad, India. Here, YOLOv7 instance segmentation model was trained on well curated trees canopy dataset (6500 images) in order to carry out the change detection. During training, evaluation metrics such as bounding box regression and mask regression loss, mean average precision (mAP) and stochastic gradient descent algorithm were used for evaluating and optimizing the performance of model. After the 500 epochs, the mAP of 0.715 and 0.699 for individual tree detection and tree canopy mask segmentation were obtained. However, by further tuning hyper parameters of the model, maximum accuracy of 80 % of trees detection with false segmentation rate of 2% on data was obtained.
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