Abstract
Ongoing advancements in computer vision, particularly in pattern recognition and scene classification, have enabled new applications in environmental monitoring. Deep learning now offers non-contact methods for assessing water quality and detecting contamination, both critical for disaster response and public health protection. This work introduces HydroVision, a deep learning-based scene classification framework that estimates optically active water quality parameters including Chlorophyll-Alpha, Chlorophylls, Colored Dissolved Organic Matter (CDOM), Phycocyanins, Suspended Sediments, and Turbidity from standard Red-Green-Blue (RGB) images of surface water. HydroVision supports early detection of contamination trends and strengthens monitoring by regulatory agencies during external environmental stressors, industrial activities, and force majeure events. The model is trained on more than 500,000 seasonally varied images collected from the United States Geological Survey Hydrologic Imagery Visualization and Information System between 2022 and 2024. This approach leverages widely available RGB imagery as a scalable, cost-effective alternative to traditional multispectral and hyperspectral remote sensing. Four state-of-the-art convolutional neural networks (VGG-16, ResNet50, MobileNetV2, DenseNet121) and a Vision Transformer are evaluated through transfer learning to identify the best-performing architecture. DenseNet121 achieves the highest validation performance, with an R2 score of 0.89 in predicting CDOM, demonstrating the framework's promise for real-world water quality monitoring across diverse conditions. While the current model is optimized for well-lit imagery, future work will focus on improving robustness under low-light and obstructed scenarios to expand its operational utility.
Abstract (translated)
计算机视觉领域的持续进步，尤其是在模式识别和场景分类方面的发展，已经为环境监测带来了新的应用。深度学习现在提供了一种非接触式的方法来评估水质并检测污染，这对于灾难应对和公共卫生保护至关重要。本文介绍了HydroVision，这是一个基于深度学习的场景分类框架，可以从表面水体的标准红绿蓝（RGB）图像中估算出包括叶绿素-A、总叶绿素、溶解有机物（CDOM）、藻青素、悬浮颗粒物和浑浊度在内的光活性水质参数。HydroVision支持早期污染趋势检测，并在外部环境压力、工业活动以及不可抗力事件期间增强监管机构的监测能力。 该模型是在超过50万张由美国地质调查局水文图像可视化信息系统（2022年至2024年间收集）提供的季节性变化图像上训练出来的。这种方法利用广泛可用的RGB图像作为传统多光谱和高光谱遥感的一种可扩展且成本效益高的替代方案。通过迁移学习，评估了四种最先进的卷积神经网络（VGG-16、ResNet50、MobileNetV2、DenseNet121）和一个视觉变换器，以确定最佳的架构。其中，DenseNet121实现了最高的验证性能，在预测CDOM时获得了R²得分为0.89的成绩，这表明该框架在不同条件下进行实际水质监测方面具有前景。 尽管当前模型针对光线充足的图像进行了优化，未来的工作将重点放在改进低光和受阻情况下的鲁棒性上，以扩大其操作实用性。
URL
https://arxiv.org/abs/2509.01882
