# Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
## 1 Introduction  
Natural gas plays a critical role in the infrastructure of Germany, as well as numerous other countries. As of 2023, natural gas accounted for a significant portion of Germany's energy production, serving as a vital energy source for millions of households and powering industrial production. Given its importance, it is imperative for pipeline operators to actively monitor their pipeline networks to ensure their operational integrity, enhance safety, and mitigate potential risks. Various monitoring tools are employed to track essential parameters such as pipeline pressure, flow rates, and temperature, enabling the detection of corrosion, leakages, and other irregularities [1; 2]. Gas pipelines are typically buried beneath the surface, which also exposes them to potential risks from human interference. For instance, construction activities involving digging or the accumulation of soil and heavy loads nearby can potentially damage the pipelines. Therefore, it is essential to monitor such external influences. Additionally, by law, pipeline operators must be aware of possible hazardous conditions near the pipeline that may cause damage. In the future, this will also be important for hydrogen pipelines, which are currently being developed and have the same monitoring needs as gas pipelines [3]. Presently, the monitoring of potential pipeline incidents is reliant on the utilization of helicopters, which conduct monthly flights along the pipeline. During these flights, human observers are responsible for identifying and documenting any observable alterations. However, this approach has several drawbacks, including high costs, environmental concerns, and the limited frequency of flights. Moreover, the pilots' estimations regarding the location of their findings lack precise GPS coordinates and are prone to human error. The considerable time gap between flights, coupled with the extended length of the pipeline, makes it challenging to accurately recall the pipeline's appearance from the previous month. Consequently, some potential incidents may go unnoticed. In recent years, access to remote sensing data has become easier. More providers have entered the market, and the number of satellites orbiting Earth keeps growing. Satellite imagery has become increasingly more cost-effective, and the temporal resolution has increased. When a satellite passes over an area of interest, it acquires an image that can be monitored, with the exception of adverse weather conditions, which may impact the availability of new images. The growing amount of available data paves the way for the use of deep learning techniques for automated pipeline monitoring. This automated approach reduces the financial burden of helicopter flights and mitigates the need for substantial human involvement, thereby reducing potential errors and environmental impact. Furthermore, the regular updates provided by remote sensing imagery enable accurate tracking of changes over time. For example, during the construction phase, it becomes feasible to report various stages of building development or effectively monitor field growth and harvest cycles. Change detection (CD) is a common task in remote sensing, where a model has to learn to detect relevant changes, given a pair of co-registered images. CD finds applications in diverse domains, including flood detection [4], monitoring glacier development [5], and urban changes [6]. Over the last decades, various models have been employed for CD [7] among which are convolutional neural networks. Recent advancements have shown promising outcomes with deep learning models based on the transformer architecture [8], particularly for high-resolution data (0.5 m) [9; 10]. It is apparent that with higher image resolution, it is easier to detect changes. Unfortunately, with current satellite constellations, acquiring high-resolution data remains expensive, and updates are less often available than low-resolution data (3 m), making it less attractive for commercial use.