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 …
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 …
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
Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …
sensing images (RSIs). To better understand the connection between three feature learning …
Ssl4eo-l: Datasets and foundation models for landsat imagery
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 …
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 …
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 …
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
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 …
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
Advances in deep learning and computer vision are making significant contributions to flood
mapping, particularly when integrated with remotely sensed data. Although existing …
mapping, particularly when integrated with remotely sensed data. Although existing …
Brain-inspired remote sensing foundation models and open problems: A comprehensive survey
The foundation model (FM) has garnered significant attention for its remarkable transfer
performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a …
performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a …
A five-year milestone: reflections on advances and limitations in GeoAI research
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) …
marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) …