## 1 Introduction  
Tailings storage facilities (TSFs) are critical in the mining industry, as they safely contain the byproducts of mineral processing, which often include toxic or environmentally hazardous materials. Proper monitoring of TSFs is essential to prevent structural failures, environmental contamination, and potential risks to surrounding communities, ensuring the long-term sustainability and safety of mining operations [1]. In recent years, several research papers introducing the internet of things (IoT), machine learning, and artificial intelligence approaches to the problem of TSF monitoring have been published [2, 3, 4, 5, 6, 7]. One of the key issues in the management and supervision system of the tailings storage facility is the process of beach formation. The technology for discharging flotation waste involves the transport of coarse-grained material (primarily from specific mining regions) mixed with water through pipelines located on the crest of the dam. As a result of this process, so-called beaches are formed, with a slight slope towards the reservoir and varying widths. The material discharged locally (at specific points) near the dam crest undergoes segregation. Coarser material settles closer to the dams, while finer material is found nearer the decant pond. The coarse material is used in the process of raising the dams. In practice, an operational manual dictates the beach formation procedures to ensure the safety of the tailings facility. The most important procedures include  
* Maintaining a minimum beach width of 200 m adjacent to the dams with a slope towards the reservoir;
* Ensuring a 0.5 m elevation difference between the dam crest and the beach surface, as well as its uniform growth;* Limiting the formation of one dam section to no longer than three weeks;
* Controlling the sequence of beach formation for every other section of the TSF. Currently, information about the beach and dam crest is obtained through site inspections conducted by personnel during periodic inspection drives. This procedure is time-consuming and requires personnel to drive along the crest and walk around certain parts of the beach. Some markers are also used to monitor whether the beach line exceeds the allowable distance from the dam crest. Consequently, there is a growing demand for quick and automated access to up-to-date, accurate, and continuous digital information on the beach line, accompanied by analytical tools supporting the beach formation process, equipped with predictive functions. An analysis of the market and the literature did not reveal any ready-made or similar solutions that could meet the current challenges faced by operators responsible for such a large-scale facility. Although it is possible to find examples of shoreline detection based on various types of data and methods. In [8; 9], a broad review of methods for defining and determining the shoreline was presented. The authors indicate various types of shoreline definitions and indicators, as well as types of data that can be used for detection, including historical photographs and maps, aerial photogrammetry and ground-based GPS measurements, terrain models with LIDAR, radar imaging (SAR), and video systems. For shoreline detection, it is possible to use multi-spectral data, as proposed by the authors of [10], where they used the Direct Difference Water Index (DDWI) and data from the LIDAR system. In turn, in [11], a method for shoreline detection using histogram equalization and adaptive thresholding techniques based on data from the Indian Remote Sensing Satellite was proposed. In [12], SAR data and neural network method were used to distinguish land and sea. Another example is [13], where the authors used video data to detect the coastline and compared four methods: Shore Line Intensity Maximum, Pixel Intensity Clustering, Artificial Neural Network, and Color Channel Divergence. Images from drones have the potential to obtain much more accurate results than, for instance, satellite data.