## 1 Introduction
### Challenges in Mangrove Monitoring  
Remote sensing technology has become an indispensable tool in mangrove monitoring and management due to its advantages of low cost, high efficiency, and broad observation range. It is widely applied in mangrove wetland mapping, community structure analysis, biomass estimation, and disaster warning [8]. However, current monitoring methods face three major challenges.  
Firstly, there are significant limitations at the dataset level. Current research primarily relies on medium- to low-resolution satellite data [9; 10], with a relatively large spatial resolution that fails to meet the needs of fine-scale monitoring. While drone data [11] and high-resolution satellite data [12] are becoming more available, annotated datasets for deep learning remain scarce. Furthermore, the lack of standardized datasets makes it difficult to directly compare results between different studies, severely constraining algorithm development and validation processes. Additionally, the acquisition of mangrove wetland monitoring data is often limited by weather conditions and satellite revisit cycles, making it challenging to accumulate high-quality training data.  
Secondly, compared to horizontal-view images captured by traditional ground cameras, remote sensing images are collected from an aerial perspective, featuring a wide imaging range, high resolution, and high information density [13]. The unique spatial distribution characteristics of mangrove wetlands also pose significant challenges for monitoring. As shown in Figure 1, they primarily grow at the land-sea interface, displaying highly fragmented distribution patterns. (Figure 1a) This spatial discontinuity significantly increases the complexity of target identification. More challengingly, mangrove wetlands exhibit remarkable scale diversity, ranging from discretely distributed small shrub communities to large-scale continuous forest areas [14]. (Figure 1c) These multi-scale characteristics place higher demands on semantic segmentation algorithms. Furthermore, due to their growth in intertidal zones, the image characteristics of mangroves change significantly with tidal variations. (Figure 1b) The same area may present entirely different image features under different tidal conditions, and this dynamically changing background environment further complicates monitoring efforts.  
Thirdly, significant challenges exist in technical processing. With the increasing resolution of remote sensing data, the data volume of single-scene images has increased dramatically [15], placing enormous pressure on computational resources. Processing high-resolution images not only requires more powerful hardware support but also demands algorithm-level optimization to improve processing efficiency. In practical applications, maintaining consistent segmentation accuracy across different scales poses higher requirements for algorithm robustness. How to achieve efficient processing while ensuring segmentation accuracy under limited computational resources remains a significant technical challenge.  
The existence of these challenges indicates the need to develop more advanced data acquisition methods, more effective processing algorithms, and more comprehensive monitoring systems. Particularly in the context of rapidly developing deep learning technology, how to fully utilize new technologies to overcome these limitations and improve the accuracy and efficiency of mangrove monitoring has become a key research direction.