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Opening the Black Box: A systematic review on explainable artificial intelligence in remote sensing
In recent years, black-box machine learning approaches have become a dominant modeling
paradigm for knowledge extraction in remote sensing. Despite the potential benefits of …
paradigm for knowledge extraction in remote sensing. Despite the potential benefits of …
Deep learning meets object-based image analysis: Tasks, challenges, strategies, and perspectives
Deep learning has gained significant attention in remote sensing, especially in pixel-or
patch-level applications. Despite initial attempts to integrate deep learning into object-based …
patch-level applications. Despite initial attempts to integrate deep learning into object-based …
A multi-modal geospatial–temporal LSTM based deep learning framework for predictive modeling of urban mobility patterns
Urban mobility prediction is crucial for optimizing resource allocation, managing
transportation systems, and planning urban development. We propose a novel framework …
transportation systems, and planning urban development. We propose a novel framework …
Tracking Urban Sprawl: A Systematic Review and Bibliometric Analysis of Spatio-Temporal Patterns Using Remote Sensing and GIS
The urban sprawl phenomenon refers to the expansion of urban areas driven by high
population growth and migration. A spatio-temporal approach is indispensable in urban …
population growth and migration. A spatio-temporal approach is indispensable in urban …
Digital twin-based applications in crop monitoring
Technological advances in agriculture, particularly the use of digital twins, are having a
significant impact on crop management. This article explores the use of digital twins in crop …
significant impact on crop management. This article explores the use of digital twins in crop …
An Empirical Study on Data Augmentation for Pixel-Wise Satellite Image Time Series Classification and Cross-Year Adaptation
Y Yuan, L Lin, Q ** and vegetation
monitoring. Despite the success of deep learning methods in SITS classification, their …
monitoring. Despite the success of deep learning methods in SITS classification, their …
Machine learning approaches to landsat change detection analysis
The Landsat mission has captured images of the Earth's surface for over 50 years, and the
data have enabled researchers to investigate a vast array of different change phenomena …
data have enabled researchers to investigate a vast array of different change phenomena …
[HTML][HTML] Bayesian Inference for Post-Processing of Remote-Sensing Image Classification
A key component of remote-sensing image analysis is image classification, which aims to
categorize images into different classes using machine-learning methods. In many …
categorize images into different classes using machine-learning methods. In many …
Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives
Deep learning has gained significant attention in remote sensing, especially in pixel-or
patch-level applications. Despite initial attempts to integrate deep learning into object-based …
patch-level applications. Despite initial attempts to integrate deep learning into object-based …
Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring
Continuous monitoring of crops and forecasting crop conditions through time series analysis
is crucial for effective agricultural management. This study proposes a framework based on …
is crucial for effective agricultural management. This study proposes a framework based on …