Exploring unsupervised domain adaptation methods for automated linear disturbance mapping

在加拿大北部广袤的北方森林和湿地地区，道路、地震勘探、管道和能源传输走廊等干扰是林地驯鹿(Rangifer Tarandus)-北方种群数量下降的主要原因。因此，深入了解这些“线性扰动”已成为加拿大研究和森林管理的重点。支持管理线性干扰的理想工具是自动生成成本效益高的地图，准确识别这种形式的森林栖息地碎片化。因此，该项目的重点是开发一种利用深度学习方法从多光谱卫星图像自动制作地图的方法。具体地说，为对卫星图像中的每个像素进行分类而设计的卷积神经网络将被用于从哨兵2号数据中产生线性干扰图。需要解决的主要研究挑战是，标签是使用昂贵的高分辨率SPOT-6卫星数据开发的，对于自由和中等分辨率的哨兵2号数据没有同等的标签。因此，该工作将开发一种无监督的领域自适应方法，其目的是从一个领域中获取数据和相应的标签，并将其适应于在相关但不同的领域中训练语义分割模型。这项工作的主要好处是改进了监测影响北方加勒比牧群的零散栖息地的方法，从而支持了重要的保护成果。其他好处包括将一般无监督领域适应从较精细的空间分辨率领域推进到较粗略的领域，提供更多机会将免费获得的哨兵-2数据用于其他相关环境监测任务，以及开发基线方法以用于频谱或时间序列分析。

In Canada's vast northern region of boreal forest and wetlands, disturbances such as roads, seismic exploration, pipelines, and energy transmission corridors are a leading cause of the decline of woodland caribou (Rangifer tarandus) - boreal population. As a result, a deep understanding of these "linear disturbances" has become a research and forest management priority in Canada. An ideal tool to support managing linear disturbances is a way to automatically generate cost-effective maps that accurately identify this form of forest habitat fragmentation. As a result, the focus of this project is to develop an approach for automated map production from multispectral satellite images using deep learning methods. Specifically, convolutional neural networks designed for classifying each pixel in a satellite image will be used to produce linear disturbance maps from Sentinel-2 data. The main research challenge to be addressed is that labels were developed using expensive high-resolution SPOT-6 satellite data, and there are no equivalent labels for the free and medium-resolution Sentinel-2 data. Thus, the proposed work will develop an unsupervised domain adaptation approach, which aims to take the data and corresponding labels from one domain and adapt it for training semantic segmentation models in a related, but different domain. The main benefit of this work is advancing methods for monitoring the fragmented habitats affecting the boreal Caribou herds at scale, thereby supporting important conservation outcomes. Additional benefits include advancing general unsupervised domain adaptation from finer spatial resolution domains to coarser ones, providing improved opportunities to use freely available Sentinel-2 data for other related environmental monitoring tasks, and developing baseline methodology to build upon for spectral or time-series analysis.