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.

翻译：估计亚马逊森林的毁林情况是一项艰巨的任务,因为该地区面积辽阔,人类难以直接接触,然而,这是一个关键问题,因为毁林导致严重的环境问题,如全球气候变化、生物多样性减少等。 为了有效解决这些问题,卫星图像是评估亚马逊森林砍伐情况的一个好选择。结合光学图像和合成孔径雷达(合成孔径雷达)图像,不论气候条件如何,观测如此大面积的地区都是可能的。在本文件中,我们用传统的UNet和综合数据处理方法提出了准确的毁林估计方法。哨兵1号、哨兵2号和大地卫星8号等不同渠道经过仔细挑选,用于培训深层神经网络。根据拟议方法,对新查询的毁林状况进行了非常精确的精确估计。