## 6 Conclusions and Future Research Directions
When addressed, these research directions will enable further developments in remote sensing and data integration with models, enabling actionable insights into the sustainable management of coasts and disaster preparedness, which will enhance the resilience of all coastal communities worldwide. In forthcoming applications, we envision the integration of a wider array of spatial datasets beyond those currently utilized in satellite missions. Reflecting the observations of [PERSON] et al. [105], geoporals at the national level in Poland and Finland provide open-access cadastral, orthophotos, and thematic maps that enable land surveyance and coastal management. Likewise, [PERSON] et al. [106] underscored the growing availability of public and volunteered geographic information (VGI) from OpenStreetMap, GeoTagging Flickr, Wikimapia, and Google Earth, complemented by authoritative data such as the USGS Earth Explorer and the Sentinel Copernicus Open Access Hub, as well as mapping agencies at the national level. These datasets--LiDAR, bathymetry, GNSS networks, InSAR, and UAV-based photogrammetry--can enable high-resolution shoreline extraction and validate AI-based outputs. Integrating them into our existing satellite and ground truth techniques may provide higher-quality spatiotemporal accuracy and promote the democratization of GIS at the level of coastal stakeholders. Upcoming works must include the incorporation of airborne LiDAR bathymmetry (ALB) methods into enhanced coastal monitoring. According to [PERSON] et al. (2019, 2021) the systems of ALB, particularly full-waveform processing and artificial neural networks, have been shown to be able to map the features of the seafloor with sub-decimeter vertical accuracy, even in highly complex underwater landscapes [107; 108]. These techniques are a good complement to satellite- or UAV-based methods, particularly in turbid water or shallow water situations where the use of passive sensors becomes difficult. The accuracy and classification ability of ALB makes it a very good subject for upcoming studies on coastal erosion, buried structure detection, and digital twins. Recent developments in Artificial Intelligence brought forth state-of-the-art techniques that can extensively support shoreline detection, erosion mapping, and environmental feature identification. For one, [PERSON] et al. introduced a context-aware Self- and Cross-Triplet Clue learning model to facilitate semantic comprehension based on graph-based context modeling [109], while [PERSON] et al. [93] designed a Manifold Regularization-Based Deep Convolutional Autoencoder (MR-DCAE) that learns low-dimensional feature-rich representations of noisy data based on manifold restrictions. These models, although originally devised for generic object detection and signal category, point to scalable AI methods that can be applied to geospatial shoreline detection, particularly in difficult coastal environments. Artificial Intelligence has greatly enhanced remote sensing applications, especially through advancing image analysis technologies in specific tasks such as shoreline segmentation and air scene classification. [PERSON] et al., for example, constructed a Triplet-Metric-Guided Multi-Scale Attention network to boost air scene classification [110]. In a similar way, [PERSON] and [PERSON] (2023) proposed a graph convolutional model of triplets to enhance image retrieval in satellite databases [111]. [PERSON] et al. also formulated DeepSA-Net, a bespoke Convolutional Neural Network (CNN) for shoreline segmentation. DeepSA-Net utilizes coordinate attention and strip-pooling mechanisms to boost feature extraction, which attains a segmentation level of over 99% mean Intersection over Union (mIoU) [112]. [PERSON] et al. demonstrated that CNNs taught using UAV data can map soil erosion with high levels of accuracy, highlighting the high potential of AI applications in supporting land degradation studies [113].