A Scalable Hybrid Intelligence Architecture for Spatiotemporal Land-Use/Land-Cover Change Detection and Driver Attribution Across Geographic Regions

A high-performance deep feature encoding network using GeoFusion-ChangeNet for land cover mapping and change detection in remote sensing imagery

This research introduces an advanced deep learning framework, GeoFusion-ChangeNet, for high-resolution land cover mapping and automatic change detection, which integrates hierarchical convolutional feature encoder with a residual deep feature backbone to effectively capture rich multi-scale spatial features.

Interpretable hybrid model combining CNN-ViT with Grad-CAM and SHAP for transparent mapping of land use changes

A novel deep learning–based framework for multi-temporal LULC classification of Al-Taif City, Saudi Arabia, using Landsat 8 imagery from 2013 to 2024 is introduced, highlighting the effectiveness of the proposed framework in delivering both high-precision classification and interpretable insights.

MoEST-Net: mixture-of-experts spatio-temporal transformer network for long-term land-use change detection in high-resolution remote sensing imagery

Long-term land-use change detection requires models that distinguish persistent land-cover transitions from seasonal appearance changes while remaining efficient over extended image sequences. MoEST-Net is a Mixture-of-Experts spatio-temporal Transformer designed for this setting. Its temporal-scale expert grid uses hi...

AdaFuse: Generative Spatiotemporal Fusion for Daily 10 m Land Surface Temperature Estimation With Domain Adaptation

LLFA-Net: a collaborative CNN-transformer framework for land cover classification using high-resolution remote sensing images

Experimental results on the ISPRS Potsdam and Vaihingen benchmark datasets show that the proposed CNN-Transformer model attains highly competitive segmentation performance while maintaining computational efficiency.

A Comparison of Machine Learning and Deep Learning Architectures with Explainable AI for Mapping Land Cover Using Sentinel-2 Satellite Data

Rapid and unplanned urbanization in South Asian cities such as Lahore, Pakistan has produced fragmented urban-agricultural landscapes in which spectral signatures of various land cover and land use (LCLU) classes overlap, degrading the performance of conventional pixel-based machine learning classifiers. This study pre...