论文标题
多读2022年森林砍伐挑战 - 森林门口
MultiEarth 2022 Deforestation Challenge -- ForestGump
论文作者
论文摘要
亚马逊森林中森林砍伐的估计是挑战任务，因为该地区的规模巨大和直接人类通道的难度。但是，这是一个至关重要的问题，因为森林砍伐会导致严重的环境问题，例如全球气候变化，生物多样性降低等。为了有效解决这些问题，卫星图像将是估计亚马逊森林砍伐的一个很好的选择。通过光学图像和合成孔径雷达（SAR）图像的组合，无论天气条件如何，都可以观察到如此庞大的区域。在本文中，我们提出了一种准确的森林砍伐估计方法，并使用常规的UNET和全面的数据处理。 Sentinel-1，Sentinel-2和Landsat 8的各种渠道被精心选择并用于训练深层神经网络。通过提出的方法，以很高的精度成功估算了新型查询的森林砍伐状态。
The estimation of deforestation in the Amazon Forest is challenge task because of the vast size of the area and the difficulty of direct human access. However, it is a crucial problem in that deforestation results in serious environmental problems such as global climate change, reduced biodiversity, etc. In order to effectively solve the problems, satellite imagery would be a good alternative to estimate the deforestation of the Amazon. With a combination of optical images and Synthetic aperture radar (SAR) images, observation of such a massive area regardless of weather conditions become possible. In this paper, we present an accurate deforestation estimation method with conventional UNet and comprehensive data processing. The diverse channels of Sentinel-1, Sentinel-2 and Landsat 8 are carefully selected and utilized to train deep neural networks. With the proposed method, deforestation status for novel queries are successfully estimated with high accuracy.
