Deforestation estimation and fire detection in the Amazon forest poses a
significant challenge due to the vast size of the area and the limited
accessibility. However, these are crucial problems that lead to severe
environmental consequences, including climate change, global warming, and
biodiversity loss. To effectively address this problem, multimodal satellite
imagery and remote sensing offer a promising solution for estimating
deforestation and detecting wildfire in the Amazonia region. This research
paper introduces a new curated dataset and a deep learning-based approach to
solve these problems using convolutional neural networks (CNNs) and
comprehensive data processing techniques. Our dataset includes curated images
and diverse channel bands from Sentinel, Landsat, VIIRS, and MODIS satellites.
We design the dataset considering different spatial and temporal resolution
requirements. Our method successfully achieves high-precision deforestation
estimation and burned area detection on unseen images from the region. Our
code, models and dataset are open source:
this https URL

利用深度卷积神经网络和多模卫星图像对亚马逊森林的砍伐估测和火灾检测问题进行了研究，并提出了一种新的数据集和处理技术。

利用卫星图像进行深度多模式学习的快速森林砍伐和烧毁区域检测

Rapid Deforestation and Burned Area Detection using Deep Multimodal  Learning on Satellite Imagery

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.

利用卫星图像和深度神经网络结合的方法，通过对 Sentinel-1，Sentinel-2 和 Landsat 8 的数据进行精细处理，成功地对亚马逊森林地区的砍伐情况进行了高准确度的估算。