Comparative machine-learning retrieval of high-precision bathymetry from multispectral remote sensing: a case study of Manzala Lagoon, Egypt

Nearshore satellite-derived bathymetry estimation using Sentinel-2 multispectral data and ensemble learning

Artificial intelligence-based water quality assessment from remote sensing images – a case study of the Thi Vai river

The findings demonstrate the feasibility of integrating remote sensing and machine learning for cost-effective and spatially continuous TSS monitoring in estuarine river systems.

Improving UAV-Derived Nearshore Bathymetry Using Geospatial Machine Learning

A framework that integrates high-resolution uncrewed aerial vehicle (UAV) multispectral imagery with geospatial machine learning to address spatial variability in reflectance-depth relationships and to mitigate performance overestimation by explicitly accounting for spatial autocorrelation is presented.

Controlled Evaluation of Sentinel-2 Annual Compositing Strategies for Deep Learning-Based Mangrove Mapping in China

Accurate national-scale mangrove mapping remains challenging because mangroves occur in narrow intertidal belts affected by tidal variation, residual clouds, water background effects, and spectral confusion with adjacent vegetation. This study evaluated five Sentinel-2 annual compositing rules for 10 m mangrove mapping...

Enhancing the performance of medium-resolution DEM-based landslide susceptibility maps with machine learning algorithms: a case study of Watauga County, North Carolina

The performance of landslide susceptibility maps relies heavily on the quality and resolution of the digital elevation model (DEM) utilized for deriving the causal factors of landslides. In many regions of the world, the unavailability of high-resolution DEMs necessitates the use of medium resolution (30 m) freely avai...

Spectral–Texture Feature Fusion With Spatial Group Cross-Validation for Sea Ice Mapping Using HY-1C Satellite Imagery

Accurate extraction of sea ice extent is of great importance for climate research, maritime navigation safety, and marine environmental monitoring. Traditional optical remote sensing methods are often limited by insufficient feature representation, while conventional machine-learning evaluations may overestimate classi...