Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives
Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …
tools and data fusion strategies has recently opened new perspectives for environmental …
[HTML][HTML] Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification
Abstract We present AiTLAS: Benchmark Arena–an open-source benchmark suite for
evaluating state-of-the-art deep learning approaches for image classification in Earth …
evaluating state-of-the-art deep learning approaches for image classification in Earth …
Skysense: A multi-modal remote sensing foundation model towards universal interpretation for earth observation imagery
Abstract Prior studies on Remote Sensing Foundation Model (RSFM) reveal immense
potential towards a generic model for Earth Observation. Nevertheless these works primarily …
potential towards a generic model for Earth Observation. Nevertheless these works primarily …
Geo-bench: Toward foundation models for earth monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on
vast amounts of unsupervised data can lead to substantial increases in generalization to …
vast amounts of unsupervised data can lead to substantial increases in generalization to …
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 …
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 …
Omnisat: Self-supervised modality fusion for earth observation
The diversity and complementarity of sensors available for Earth Observations (EO) calls for
develo** bespoke self-supervised multimodal learning approaches. However, current …
develo** bespoke self-supervised multimodal learning approaches. However, current …
CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders
A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled,
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …
spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable …
Learning a deep ensemble network with band importance for hyperspectral object tracking
Attributing to material identification ability powered by a large number of spectral bands,
hyperspectral videos (HSVs) have great potential for object tracking. Most hyperspectral …
hyperspectral videos (HSVs) have great potential for object tracking. Most hyperspectral …
MMEarth: Exploring multi-modal pretext tasks for geospatial representation learning
The volume of unlabelled Earth observation (EO) data is huge, but many important
applications lack labelled training data. However, EO data offers the unique opportunity to …
applications lack labelled training data. However, EO data offers the unique opportunity to …