Differently from conventional procedures, the proposed solution advocates for
a groundbreaking paradigm in water quality monitoring through the integration
of satellite Remote Sensing (RS) data, Artificial Intelligence (AI) techniques,
and onboard processing. The objective is to offer nearly real-time detection of
contaminants in coastal waters addressing a significant gap in the existing
literature. Moreover, the expected outcomes include substantial advancements in
environmental monitoring, public health protection, and resource conservation.
The specific focus of our study is on the estimation of Turbidity and pH
parameters, for their implications on human and aquatic health. Nevertheless,
the designed framework can be extended to include other parameters of interest
in the water environment and beyond. Originating from our participation in the
European Space Agency (ESA) OrbitalAI Challenge, this article describes the
distinctive opportunities and issues for the contaminants monitoring on the
Phisat-2 mission. The specific characteristics of this mission, with the tools
made available, will be presented, with the methodology proposed by the authors
for the onboard monitoring of water contaminants in near real-time. Preliminary
promising results are discussed and in progress and future work introduced.

通过卫星遥感数据、人工智能技术和机载处理，提供近实时检测沿海水体中污染物的突破性水质监测方案，以填补现有文献中存在的重要空白，并在环境监测、公共卫生保护和资源保护方面取得实质性进展。

未来 Phisat-2 任务中海岸水体污染的近实时监测的 AI 技术

AI techniques for near real-time monitoring of contaminants in coastal  waters on board future Phisat-2 mission

Water is the lifeblood of river networks, and its quality plays a crucial
role in sustaining both aquatic ecosystems and human societies. Real-time
monitoring of water quality is increasingly reliant on in-situ sensor
technology. Anomaly detection is crucial for identifying erroneous patterns in
sensor data, but can be a challenging task due to the complexity and
variability of the data, even under normal conditions. This paper presents a
solution to the challenging task of anomaly detection for river network sensor
data, which is essential for accurate and continuous monitoring. We use a graph
neural network model, the recently proposed Graph Deviation Network (GDN),
which employs graph attention-based forecasting to capture the complex
spatio-temporal relationships between sensors. We propose an alternate anomaly
scoring method, GDN+, based on the learned graph. To evaluate the model's
efficacy, we introduce new benchmarking simulation experiments with
highly-sophisticated dependency structures and subsequence anomalies of various
types. We further examine the strengths and weaknesses of this baseline
approach, GDN, in comparison to other benchmarking methods on complex
real-world river network data. Findings suggest that GDN+ outperforms the
baseline approach in high-dimensional data, while also providing improved
interpretability. We also introduce software called gnnad.

本文介绍了一种针对河流传感器数据的异常检测解决方案，使用图神经网络模型 GDN + 来捕捉传感器之间的复杂时空关系，并针对河流网络数据中的高维数据进行评估，发现 GDN + 比基准方法表现更优越和提供更好的可解释性。同时，本文还介绍了 gnnad 软件。