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 …
Deep learning in environmental remote sensing: Achievements and challenges
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …
environmental remote sensing research. With an increasing amount of “big data” from earth …
Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
modality-dominated remote sensing (RS) applications, especially with an emphasis on …
[HTML][HTML] Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost
Z Li - Computers, Environment and Urban Systems, 2022 - Elsevier
Abstract Machine learning and artificial intelligence (ML/AI), previously considered black box
approaches, are becoming more interpretable, as a result of the recent advances in …
approaches, are becoming more interpretable, as a result of the recent advances in …
Swin transformer embedding UNet for remote sensing image semantic segmentation
X He, Y Zhou, J Zhao, D Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Global context information is essential for the semantic segmentation of remote sensing (RS)
images. However, most existing methods rely on a convolutional neural network (CNN) …
images. However, most existing methods rely on a convolutional neural network (CNN) …
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …
of practical applications, such as land cover mapping, urban change detection …
Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to
mimic different conditions and scales, with the resulting models used for various tasks with …
mimic different conditions and scales, with the resulting models used for various tasks with …
Single-source domain expansion network for cross-scene hyperspectral image classification
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing
attention. It is necessary to train a model only on source domain (SD) and directly …
attention. It is necessary to train a model only on source domain (SD) and directly …
LoveDA: A remote sensing land-cover dataset for domain adaptive semantic segmentation
Deep learning approaches have shown promising results in remote sensing high spatial
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …
[HTML][HTML] The segment anything model (sam) for remote sensing applications: From zero to one shot
Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …
advance the application of the Segment Anything Model (SAM), an innovative image …