Enhancing land cover mapping and monitoring: An interactive and explainable machine learning approach using Google Earth Engine
Artificial intelligence (AI) and machine learning (ML) have been applied to solve various
remote sensing problems. To fully leverage the power of AI and ML to tackle impactful …
remote sensing problems. To fully leverage the power of AI and ML to tackle impactful …
Integration of machine learning and open access geospatial data for land cover mapping
M Mardani, H Mardani, L De Simone, S Varas, N Kita… - Remote Sensing, 2019 - mdpi.com
In-time and accurate monitoring of land cover and land use are essential tools for countries
to achieve sustainable food production. However, many developing countries are struggling …
to achieve sustainable food production. However, many developing countries are struggling …
[HTML][HTML] Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes
Deriving land cover from remotely sensed data is fundamental to many operational mapping
and reporting programs as well as providing core information to support science activities …
and reporting programs as well as providing core information to support science activities …
Google Earth Engine and artificial intelligence (AI): a comprehensive review
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 …
global climate change, risk assessment and vulnerability reduction of natural hazards …
[HTML][HTML] Urban land use and land cover classification with interpretable machine learning–A case study using Sentinel-2 and auxiliary data
Abstract The European Commission launch of the twin Sentinel-2 satellites provides new
opportunities for land use and land cover (LULC) classification because of the ready …
opportunities for land use and land cover (LULC) classification because of the ready …
A data-driven machine learning-based approach for urban land cover change modeling: A case of Khulna City Corporation area
Land use and land cover (LULC) changes have significant consequences on habitat and the
environment. Past studies developed several LULC change models to identify the factors …
environment. Past studies developed several LULC change models to identify the factors …
Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities
Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …
particularly machine learning algorithms, range from initial image processing to high-level …
A review of practical ai for remote sensing in earth sciences
Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for
revolutionizing data analysis and applications in many domains of Earth sciences. This …
revolutionizing data analysis and applications in many domains of Earth sciences. This …
Interpretable deep learning framework for land use and land cover classification in remote sensing using SHAP
An interpretable deep learning framework for land use and land cover (LULC) classification
in remote sensing using Shapley additive explanations (SHAPs) is introduced. It utilizes a …
in remote sensing using Shapley additive explanations (SHAPs) is introduced. It utilizes a …
Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine
B Feizizadeh, D Omarzadeh… - Journal of …, 2023 - Taylor & Francis
With the recent advances in earth observation technologies, the increasing availability of
data from more and more different satellite sensors as well as progress in semi-automated …
data from more and more different satellite sensors as well as progress in semi-automated …