Self-supervised learning in remote sensing: A review

Y Wang, CM Albrecht, NAA Braham… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
In deep learning research, self-supervised learning (SSL) has received great attention,
triggering interest within both the computer vision and remote sensing communities. While …

[HTML][HTML] Unsupervised machine learning in urban studies: A systematic review of applications

J Wang, F Biljecki - Cities, 2022 - Elsevier
Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …

Self-supervised remote sensing feature learning: Learning paradigms, challenges, and future works

C Tao, J Qi, M Guo, Q Zhu, H Li - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

Ssl4eo-l: Datasets and foundation models for landsat imagery

A Stewart, N Lehmann, I Corley… - Advances in …, 2024 - proceedings.neurips.cc
The Landsat program is the longest-running Earth observation program in history, with 50+
years of data acquisition by 8 satellites. The multispectral imagery captured by sensors …

FWENet: a deep convolutional neural network for flood water body extraction based on SAR images

J Wang, S Wang, F Wang, Y Zhou, Z Wang… - … Journal of Digital …, 2022 - Taylor & Francis
As one of the most severe natural disasters in the world, floods caused substantial economic
losses and casualties every year. Timely and accurate acquisition of flood inundation extent …

Taking artificial intelligence into space through objective selection of hyperspectral earth observation applications: To bring the “brain” close to the “eyes” of satellite …

AM Wijata, MF Foulon, Y Bobichon… - … and Remote Sensing …, 2023 - ieeexplore.ieee.org
Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI)
bring exciting opportunities to various fields of science and industry that can directly benefit …

An efficient urban flood mapping framework towards disaster response driven by weakly supervised semantic segmentation with decoupled training samples

Y He, J Wang, Y Zhang, C Liao - ISPRS Journal of Photogrammetry and …, 2024 - Elsevier
Despite the proven effectiveness of data-driven deep learning techniques in urban flood
mapping, the availability of annotation data remains a critical factor impeding their timeliness …

[HTML][HTML] Near real-time flood mapping with weakly supervised machine learning

J Vongkusolkit, B Peng, M Wu, Q Huang… - Remote Sensing, 2023 - mdpi.com
Advances in deep learning and computer vision are making significant contributions to flood
mapping, particularly when integrated with remotely sensed data. Although existing …

Brain-inspired remote sensing foundation models and open problems: A comprehensive survey

L Jiao, Z Huang, X Lu, X Liu, Y Yang… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
The foundation model (FM) has garnered significant attention for its remarkable transfer
performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a …

A five-year milestone: reflections on advances and limitations in GeoAI research

Y Hu, M Goodchild, AX Zhu, M Yuan, O Aydin… - Annals of …, 2024 - Taylor & Francis
ABSTRACT The Annual Meeting of the American Association of Geographers (AAG) in 2023
marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) …