Google Earth Engine and artificial intelligence (AI): a comprehensive review

L Yang, J Driscol, S Sarigai, Q Wu, H Chen, CD Lippitt - Remote Sensing, 2022 - mdpi.com
Remote sensing (RS) plays an important role gathering data in many critical domains (eg,
global climate change, risk assessment and vulnerability reduction of natural hazards …

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

H Tamiminia, B Salehi, M Mahdianpari… - ISPRS journal of …, 2020 - Elsevier
Abstract Google Earth Engine (GEE) is a cloud-based geospatial processing platform for
large-scale environmental monitoring and analysis. The free-to-use GEE platform provides …

[HTML][HTML] The state of the world's beaches

A Luijendijk, G Hagenaars, R Ranasinghe, F Baart… - Scientific reports, 2018 - nature.com
Coastal zones constitute one of the most heavily populated and developed land zones in the
world. Despite the utility and economic benefits that coasts provide, there is no reliable …

[HTML][HTML] CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery

K Vos, KD Splinter, MD Harley, JA Simmons… - … Modelling & Software, 2019 - Elsevier
CoastSat is an open-source software toolkit written in Python that enables the user to obtain
time-series of shoreline position at any sandy coastline worldwide from 30+ years (and …

[HTML][HTML] Mapping Australia's dynamic coastline at mean sea level using three decades of Landsat imagery

R Bishop-Taylor, R Nanson, S Sagar… - Remote Sensing of …, 2021 - Elsevier
Accurate, robust and consistent coastline mapping is critical for characterising and
managing coastal change. Satellite earth observation provides an unparalleled source of …

Machine learning classification of mediterranean forest habitats in google earth engine based on seasonal sentinel-2 time-series and input image composition …

S Praticò, F Solano, S Di Fazio, G Modica - Remote sensing, 2021 - mdpi.com
The sustainable management of natural heritage is presently considered a global strategic
issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) …

Multispectral satellite imagery and machine learning for the extraction of shoreline indicators

E McAllister, A Payo, A Novellino, T Dolphin… - Coastal …, 2022 - Elsevier
Abstract Analysis of shoreline change is fundamental to a broad range of investigations
undertaken by coastal scientists, coastal engineers, and coastal managers. Multispectral …

Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery

K Vos, MD Harley, KD Splinter, JA Simmons… - Coastal …, 2019 - Elsevier
The ability to repeatedly observe and quantify the changing position of the shoreline is key
to present-day coastal management and future coastal planning. This study evaluates the …

Benchmarking satellite-derived shoreline mapping algorithms

K Vos, KD Splinter, J Palomar-Vázquez… - … Earth & Environment, 2023 - nature.com
Satellite remote sensing is becoming a widely used monitoring technique in coastal
sciences. Yet, no benchmarking studies exist that compare the performance of popular …

[HTML][HTML] Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago, China

C Chen, J Liang, F Xie, Z Hu, W Sun, G Yang… - International Journal of …, 2022 - Elsevier
The acquisition of dynamic information on the coastline is of great significance for the
Zhoushan archipelago. However, a large amount of suspended sediments, a tortuous …