Conference Agenda

Kelp forests are vital marine habitats with significant ecological, cultural, and economic importance. These ecosystems, found along coastlines, are susceptible to regional and global stressors (such as coastal development and climate change). This paper presents Satellite-based Kelp Mapping (SKeMa), a novel framework for automatically mapping canopy-forming kelp forests using Sentinel-2 satellite imagery along the British Columbia coast, specifically to support First Nations marine planning for these species. SKeMa employs a deep learning semantic segmentation model, offering an efficient alternative to traditional, labor-intensive, and time-consuming kelp mapping methods. A cross-validation study with independent test sets yields a mean Intersection over Union (IoU) of 0.5326, demonstrating the model’s capability to detect kelp canopies across diverse coastal regions, particularly for larger kelp beds.

The research extended DualSeq, an advanced

machine-learning model for predicting sea surface temperature

(SST), crucial for understanding oceanic ecosystems and climate

patterns. Traditional SST prediction methods typically employ

time-series regressions focusing on nonlinear temporal patterns,

but often overlook vital spatial correlations in SST dynamics,

limiting their accuracy. DualSeq addresses this by integrating

spatial and temporal uncertainty quantification, with a particular

focus on the Arabian Sea. It utilises LSTM and GRU networks to

effectively harness the SEVIRI-IO-SST dataset, which contains

five years of remote-sensing data. A distinctive aspect of DualSeq

is its incorporation of a weighted normalized linear equation,

which significantly improves the accuracy of SST predictions and

enhances the dependability of spatial and temporal uncertainty

assessments. The model stands out in its ability to forecast up to

one month in advance, significantly outperforming others. For 1-

month forecasts, DualSeq shows a remarkable R² value of 0.983,

surpassing the LSTM-attention model by 7.4% and reducing

RMSE and MAE by about 65.4% and 82.4%, respectively.

This performance illustrates DualSeq’s superior capability in

capturing both short-term and long-term uncertainties in SST

forecasting.

This study presents a Station-Centric Geographically weighted Regression (SCGWR) framework for Chlorophyll-a prediction in the optically complex waters of the Gulf of İzmir using Sentinel-2 imagery. Unlike traditional global multiple regression model, the proposed approach calibrates an individual model for each sampling station while using 16 outer Moore-neighbor pixels (range 2) from surrounding stations as independent validation data in the model optimization, thereby preventing adjacency bias and information leakage in performance assessment. Compared to multiple linear regression (MLR) against 20 independent in-situ measurements, SCGWR method offers a robust, reproducible alternative for local-scale water-quality mapping in coastal environments where bio-optical variability is high.

Coastal areas play an important role economically, socially and environmentally due to their many functions.

However, these regions are at risk of erosion, which is further exacerbated by human-driven climate change.

Tracking and monitoring coastal boundaries enable efficient allocation of conservation and protection efforts.

Due to the vast size and complexity of coastal areas, on-site monitoring to track erosion is inefficient.

Artificial intelligence has shown impressive results in segmenting and extracting these boundaries from remote sensing imagery.

Historical remote sensing data make it possible to track long-term erosion but remain challenging due to the coarse resolution of older data.

Our work proposes studying the impact of super-resolution on coastal boundary segmentation using high-resolution imagery.

ESRGAN and SRCNN have proven highly beneficial in improving the quality of coarse-resolution samples, achieving superior performance compared to bicubic interpolation across scaling factors ranging from ×2 to ×12.

ESRGAN super-resolved samples achieved F1-scores ranging from 97.75% to 89.92% for scaling factors ×2 to ×12, while bicubic interpolation achieved between 97.34% and 65.27%.

These improvements demonstrate that SR enhances boundary delineation and robustness across scales.

Our work also explores the applicability of tracking erosion through historical data.

Results demonstrate a coastal boundary change of 0.23 m per year over seven years, which is on par with expected values.

This study introduces a novel framework that integrates a region-aware Full-Waveform Figure Descriptor (FWFD) with a Convolutional Vision Transformer (CvT) for underwater terrain classification using bathymetric LiDAR data. The FWFD converts sequential waveform returns into a multi-directional image-like representation, enabling the preservation of spatial correlations among neighboring laser footprints. By combining convolutional token embedding and self-attention mechanisms, the CvT effectively learns both local and global waveform features. Experiments on a YellowScan full-waveform LiDAR dataset over coastal Australia demonstrate that the proposed FWFD-CvT model achieves 95.55 % overall accuracy under moderate waveform smoothing and exceeds 98 % accuracy for underwater objects. The framework shows robust performance across complex seafloor morphologies and maintains consistency in mixed land-water environments. This research contributes a transferable paradigm for region-aware waveform interpretation and establishes a foundation for extending full-waveform analysis to terrestrial, multispectral, and topographic LiDAR applications requiring fine-scale surface characterization.

Shallow-water cultural heritage occupies a dynamic land-sea interface where coastal erosion, sediment transport, limited visibility and burial processes hinder conventional archaeological investigation. This paper presents an integrated geoinformatics framework for reconstructing the cultural dynamics of coastal and shallow submerged archaeological landscapes in southeastern Crete, Greece. The methodology combines multispectral remote sensing, satellite-derived and in situ bathymetry, UAV and shallow-water photogrammetry, marine geophysics, GIS-based coastal vulnerability, fuzzy logic multi-criteria risk assessment and digital dissemination through augmented reality. The workflow was applied at five representative case studies, including Stomio, Ierapetra harbour, Koufonisi, Chryse and associated coastal sectors. Optical data from Pleiades-1A, PlanetScope, and Sentinel-2A were used for shoreline mapping, feature enhancement, and satellite-derived bathymetry. Geophysical and bathymetric surveys covered more the 4.5 and 10 hectares respectively. UAV photogrammetry produced high resolution orthomosaics, while the proposed experimental Remote Control (RC) boat extends documentation potential to very shallow submerged environments. Integrated interpretation clarified palaeo-shorelines, submerged harbour structures, fish tanks, architectural continuities and archaeological risk hotspots. The results demonstrate a scalable and transferable framework for documenting, interpreting, monitoring, and communicating endangered shallow-water cultural landscapes.