Artificial intelligence for geoscience: Progress, challenges and perspectives
This paper explores the evolution of geoscientific inquiry, tracing the progression from
traditional physics-based models to modern data-driven approaches facilitated by significant …
traditional physics-based models to modern data-driven approaches facilitated by significant …
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 …
Masked vision transformers for hyperspectral image classification
Transformer architectures have become state-of-the-art models in computer vision and
natural language processing. To a significant degree, their success can be attributed to self …
natural language processing. To a significant degree, their success can be attributed to self …
Remoteclip: A vision language foundation model for remote sensing
General-purpose foundation models have led to recent breakthroughs in artificial
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …
intelligence (AI). In remote sensing, self-supervised learning (SSL) and masked image …
A billion-scale foundation model for remote sensing images
As the potential of foundation models in visual tasks has garnered significant attention,
pretraining these models before downstream tasks has become a crucial step. The three key …
pretraining these models before downstream tasks has become a crucial step. The three key …
SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets]
Self-supervised pretraining bears the potential to generate expressive representations from
large-scale Earth observation (EO) data without human annotation. However, most existing …
large-scale Earth observation (EO) data without human annotation. However, most existing …
Firerisk: A remote sensing dataset for fire risk assessment with benchmarks using supervised and self-supervised learning
In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
[HTML][HTML] A Bayesian approach for remote sensing of chlorophyll-a and associated retrieval uncertainty in oligotrophic and mesotrophic lakes
Satellite remote sensing of chlorophyll-a concentration (chla) in oligotrophic and
mesotrophic lakes faces uncertainties from sources such as atmospheric correction …
mesotrophic lakes faces uncertainties from sources such as atmospheric correction …
Vision-language models in remote sensing: Current progress and future trends
The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-
4) have sparked a wave of interest and research in the field of large language models …
4) have sparked a wave of interest and research in the field of large language models …
Cmid: A unified self-supervised learning framework for remote sensing image understanding
Self-supervised learning (SSL) has gained wide-spread attention in the remote sensing (RS)
and Earth observation (EO) communities owing to its ability to learn task-agnostic …
and Earth observation (EO) communities owing to its ability to learn task-agnostic …