# Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
## 2 Risks of Natural Gas Pipelines  
Our dataset comprises image pairs from the PlanetScope API, taken at different timesteps from the same location and their corresponding ground truth (GT) annotations. Figure 1 shows a sample from this dataset. In Germany, there are 540,000 km of pipelines providing households and factories with energy [11]. Typically, pipelines are located beneath the ground, although they may also be above ground or underwater. To collect the dataset, we initially determined the targeted coverage areas, focusing on mainland pipelines in Germany, where the external influence of human activities is the greatest. We proceeded to identify the relevant types of changes for pipeline monitoring. As a starting point, we had access to the reports from helicopter flights used previously to monitor the pipelines. Construction sites comprised the majority of the changes observed, with various agricultural changes, such as farmers piling up the harvest, following closely. Drawing from these helicopter reports, we compiled a comprehensive list of all previously observed changes. Additionally, we acknowledge the existence of other changes a human can detect that are not on the list. For these, we rely on human expertise to decide if they can pose a risk. Using this list, we searched for changes seen at varying resolutions including on high-resolution (0.5 m) images. Pipelines in cities often branch to connect households, while larger direct pipelines link different cities. Our dataset includes rural and urban areas, so a model can learn to capture changes in both. To achieve this, we randomly selected patches from central and southern Europe, focusing on Germany, covering various landscapes and changes. We used Google Earth (we utilized Google Earth Pro for desktop (earth.google.com)) and its timestep function to locate the changes. For each identified change on Google Earth, we recorded the time of its first appearance and obtained two images at the same location from the PlanetScope API, with a resolution of 3 m. The first image (_before_ image) does not include the change, while the second one (_after_ image) does. A human annotator created a binary change mask for each change as the GT. We gathered 1372 _before_ and _after_ pairs and their binary masks and randomly split them into 80% for training, 10% for validation, and 10% for testing. Collecting data for pipeline monitoring poses a challenging task since precisely specifying relevant changes is non-trivial. For instance, variations in sun exposure may lead to visible changes caused by reflected light from buildings or cars, which are no risk to the pipeline. Additionally, sensor degradation and other effects can subtly alter the pixel values of the spectral images [12], which should not impact the model's predictions. Defining the exact start of construction sites presents another challenge, as construction typically involves multiple stages such as vegetation or existing building removal, construction progress, and ultimately, the completion of the roof. Consequently, human annotators must determine when observed changes are noteworthy, leading to noisy labeling based on human judgment. Figure 1: **A new construction site:** Shown are satellite images _before_ and _after_ a new construction started. The task is to produce a binary map indicating the change, i.e., the ground truth (GT), which is shown here as a red overlay over the _after_ image. Another challenge is that small objects might not be visible on 3 m resolution images. For example, in the helicopter reports, there was a fallen perch next to a pipeline. Even for humans, detecting such a change on a 3 m resolution will be extremely challenging. Looking at higher-resolution images to find changes and then finding them at a lower resolution helped us cover smaller changes in the dataset. In general, though, the bigger the change, the higher its risk potential; therefore, we assume that all noteworthy changes can be seen with a 3 m resolution.