[HTML][HTML] Deep learning for urban land use category classification: A review and experimental assessment
Mapping the distribution, pattern, and composition of urban land use categories plays a
valuable role in understanding urban environmental dynamics and facilitating sustainable …
valuable role in understanding urban environmental dynamics and facilitating sustainable …
Long-term spatiotemporal mapping in lacustrine environment by remote sensing: Review with case study, challenges, and future directions
L Lai, Y Liu, Y Zhang, Z Cao, Y Yin, X Chen, J Jin, S Wu - Water Research, 2024 - Elsevier
Satellite remote sensing, unlike traditional ship-based sampling, possess the advantage of
revisit capabilities and provides over 40 years of data support for observing lake …
revisit capabilities and provides over 40 years of data support for observing lake …
[HTML][HTML] Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery
In aquatic remote sensing, algorithms commonly used to map environmental variables rely
on assumptions regarding the optical environment. Specifically, some algorithms assume …
on assumptions regarding the optical environment. Specifically, some algorithms assume …
Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for …
Abstract Long-term, accurate, stable, and continuous aerosol records from space are a
major requirement for climate and atmospheric environment research. Due to the limited …
major requirement for climate and atmospheric environment research. Due to the limited …
[HTML][HTML] Impacts of droughts and human activities on water quantity and quality: Remote sensing observations of Lake Qadisiyah, Iraq
Water quantity and quality in lakes are closely linked to the compounding effects of climate
change and human activities in their catchments, especially for lakes located in semi-arid …
change and human activities in their catchments, especially for lakes located in semi-arid …
Flood inundation monitoring using multi-source satellite imagery: A knowledge transfer strategy for heterogeneous image change detection
B Zhao, H Sui, J Liu, W Shi, W Wang, C Xu… - Remote Sensing of …, 2024 - Elsevier
Flood emergency mapping is essential for flood management, often requiring near real-time
extraction of large-scale flood extents by combining pre-and post-event multi-source remote …
extraction of large-scale flood extents by combining pre-and post-event multi-source remote …
Subfield-level crop yield mapping without ground truth data: A scale transfer framework
Ongoing advances in satellite remote sensing data and machine learning methods have
enabled crop yield estimation at various spatial and temporal resolutions. While yield …
enabled crop yield estimation at various spatial and temporal resolutions. While yield …
Applications of knowledge distillation in remote sensing: A survey
With the ever-growing complexity of models in the field of remote sensing (RS), there is an
increasing demand for solutions that balance model accuracy with computational efficiency …
increasing demand for solutions that balance model accuracy with computational efficiency …
Ai foundation models in remote sensing: A survey
Artificial Intelligence (AI) technologies have profoundly transformed the field of remote
sensing, revolutionizing data collection, processing, and analysis. Traditionally reliant on …
sensing, revolutionizing data collection, processing, and analysis. Traditionally reliant on …
Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning
F Huber, A Inderka, V Steinhage - Sensors, 2024 - mdpi.com
Remote sensing data represent one of the most important sources for automized yield
prediction. High temporal and spatial resolution, historical record availability, reliability, and …
prediction. High temporal and spatial resolution, historical record availability, reliability, and …