# Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
## 5 Related Work  
**Change detection** is an integral field in remote sensing with applications to various domains such as flood detection [4], glacier observation [5], and urban changes [6]. Given a pair of co-registered images, a model has to find the differences between them. What a change is depends on the specific task. As the taken images usually come with non-relevant changes, such as sensor degradation [12], a model has to learn which changes are of interest. Other works utilize a series of images to track changes over time rather than using a single pair of _before_ and _after_ images [20].  
Over the last decades, the whole bandwidth of machine learning models has been used for CD in remote sensing [7]. In recent years, though, with the successes of deep learning, the focus has shifted towards these methods, and they have received much interest in the remote sensing community [21]. Various works focus on CD via deep learning [22], and CNNs especially are often applied for CD. With the rise of transformer architectures in natural language processing [8] and computer vision [15], these models have been deployed for remote-sensing tasks as well [23].  
**Transformers for CD**: Multiple works [9; 10; 24; 25; 26] tackle CD with transformer-based architectures using some variation of the attention mechanism used in [8]. All of these works employ a Siamese-like structure that takes as input two images to produce a binary change map. Due to the successes of the vision transformer, there is an ongoing debate on whether to use CNNs or vision transformer-based methods for vision tasks [27]. In remote sensing, these principles are often seen in a combined way: The Bitemporal Image Transformer [10] uses a hybrid approach combining CNN features with the attention mechanism. ChangeFormer [9] employs a hierarchical transformer on different scales and learns a difference module. It applies the attention mechanisms to features extracted from a CNN. Works introducing transformer-based architectures have performed well on high-resolution data, while little work has been carried out on low-resolution data.  
**Pipeline monitoring** is a broad field spanning many engineering disciplines. Many methods have been developed for monitoring the internal state of a pipeline [1; 2] to detect abnormalities such as corrosion or leakages. To the best of our knowledge, methods for tracking external influences that can be harmful, such as construction sites next to a pipeline, are unexplored as of yet. The authors of [28] evaluate the potential use of technologies for construction site progress monitoring using remote sensing technologies. A related field is urbanization tracking, which is the closest to our setting [29].  
Various CD datasets exist for urbanization tracking [17; 30; 31; 32]. These datasets are benchmarks in various state-of-the-art CD models [9; 10], and they all contain high-resolution images. One exception is the OSCD dataset covering low-resolution images with 10-60 m resolution [33]. Furthermore, the time interval varies among these datasets. Generally, there are longer intervals between the _before_ and _after_ images compared to our dataset, where time intervals are around one week. In the LEVIR-CD dataset [17], for example, many _before_ images show undeveloped land, while the _after_ images show finished settlements. In contrast, our dataset includes _after_ images with recently started construction sites.