Abstract
Measuring the connectivity of water in rivers and streams is essential for effective water resource management. Increased extreme weather events associated with climate change can result in alterations to river and stream connectivity. While traditional stream flow gauges are costly to deploy and limited to large river bodies, trail camera methods are a low-cost and easily deployed alternative to collect hourly data. Image capturing, however requires stream ecologists to manually curate (select and label) tens of thousands of images per year. To improve this workflow, we developed an automated instream trail camera image classification system consisting of three parts: (1) image processing, (2) image augmentation and (3) machine learning. The image preprocessing consists of seven image quality filters, foliage-based luma variance reduction, resizing and bottom-center cropping. Images are balanced using variable amount of generative augmentation using diffusion models and then passed to a machine learning classification model in labeled form. By using the vision transformer architecture and temporal image enhancement in our framework, we are able to increase the 75% base accuracy to 90% for a new unseen site image. We make use of a dataset captured and labeled by staff from the Connecticut Department of Energy and Environmental Protection between 2018-2020. Our results indicate that a combination of temporal image processing and attention-based models are effective at classifying unseen river connectivity images.
Abstract (translated)
测量河流和溪流中水的连通性对于有效的水资源管理至关重要。与气候变化相关的极端天气事件增多可能会导致河流和溪流的连通性发生变化。虽然传统的流量监测设备部署成本高且仅限于大型河体，但使用追踪相机方法是一种低成本且易于部署的替代方案，可以收集每小时的数据。然而，图像捕捉需要溪流生态学家每年手动整理（选择并标注）成千上万张图片。 为了改进这一工作流程，我们开发了一个自动化的河中追踪相机图像分类系统，包括三个部分：(1) 图像处理；(2) 图像增强和 (3) 机器学习。图像预处理由七种图像质量过滤器组成、基于植被的亮度差异减少、调整大小以及底部居中的裁剪操作。使用生成式增强技术（通过扩散模型）以不同的程度平衡数据集，然后将标记后的图片传递给机器学习分类模型。借助我们的框架中使用的视觉变压器架构和时间图像改进技术，我们能够提高基础准确率75%，对于新未见过的地点图像达到90%。 我们利用了由美国康涅狄格州能源与环境部员工在2018年至2020年间捕获并标注的数据集。我们的研究结果表明，结合时间序列图像处理和基于注意力的模型可以有效地分类未见过的河流连通性图片。
URL
https://arxiv.org/abs/2502.00474
