论文标题
通过卷积神经网络，全球规模的城市地区的高分辨率土地覆盖地图
Very High Resolution Land Cover Mapping of Urban Areas at Global Scale with Convolutional Neural Networks
论文作者
论文摘要
本文介绍了一种方法，可以从非常高分辨率的图像和有限的嘈杂标记数据中生成7级城市地区的土地覆盖图。目的是为具有以下类别的大面积（法国部门）的分割图：沥青，裸土，建筑物，草地，矿物质材料（可渗透的人工化区域），20厘米空中图像和数字高度模型的森林和水。我们在汇总数据库，半自动分类和手动注释的一些感兴趣领域创建了一个培训数据集，以在每个班级中获得完整的地面真相。对不同编码器架构（带有重新编码器的U-NET，U-NET，DEEPLAB V3+）进行了比较研究，具有不同的损失函数。最终产品是一个高度有价值的土地覆盖图，该图从模型预测计算出来，在矢量化之前缝合在一起，二进制和精制。
This paper describes a methodology to produce a 7-classes land cover map of urban areas from very high resolution images and limited noisy labeled data. The objective is to make a segmentation map of a large area (a french department) with the following classes: asphalt, bare soil, building, grassland, mineral material (permeable artificialized areas), forest and water from 20cm aerial images and Digital Height Model. We created a training dataset on a few areas of interest aggregating databases, semi-automatic classification, and manual annotation to get a complete ground truth in each class. A comparative study of different encoder-decoder architectures (U-Net, U-Net with Resnet encoders, Deeplab v3+) is presented with different loss functions. The final product is a highly valuable land cover map computed from model predictions stitched together, binarized, and refined before vectorization.
