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Artificial Intelligence to Advance Earth Observation: A review of models, recent trends, and pathways forward
Earth observation (EO) is increasingly used for map** and monitoring processes
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …
occurring at the surface of Earth. Data acquired by satellites nowadays allow us to have a …
Cooperative perception with V2V communication for autonomous vehicles
Occlusion is a critical problem in the Autonomous Driving System. Solving this problem
requires robust collaboration among autonomous vehicles traveling on the same roads …
requires robust collaboration among autonomous vehicles traveling on the same roads …
There are no data like more data: Datasets for deep learning in earth observation
Carefully curated and annotated datasets are the foundation of machine learning (ML), with
particularly data-hungry deep neural networks forming the core of what is often called …
particularly data-hungry deep neural networks forming the core of what is often called …
2023 ieee grss data fusion contest: Large-scale fine-grained building classification for semantic urban reconstruction [technical committees]
Buildings are essential components of urban areas. While research on the extraction and 3D
reconstruction of buildings is widely conducted, information on the fine-grained roof types of …
reconstruction of buildings is widely conducted, information on the fine-grained roof types of …
[HTML][HTML] ResDepth: A deep residual prior for 3D reconstruction from high-resolution satellite images
Modern optical satellite sensors enable high-resolution stereo reconstruction from space.
But the challenging imaging conditions when observing the Earth from space push stereo …
But the challenging imaging conditions when observing the Earth from space push stereo …
The outcome of the 2021 ieee grss data fusion contest—track msd: Multitemporal semantic change detection
We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021)
organized by the Image Analysis and Data Fusion Technical Committee of the IEEE …
organized by the Image Analysis and Data Fusion Technical Committee of the IEEE …
Learning mutual modulation for self-supervised cross-modal super-resolution
Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring
paired training data, but is challenging because only low-resolution (LR) source and high …
paired training data, but is challenging because only low-resolution (LR) source and high …
A mutual information domain adaptation network for remotely sensed semantic segmentation
Although deep learning has made semantic segmentation of very-high-resolution (VHR)
remote sensing (RS) images practical and efficient, its large-scale application is still limited …
remote sensing (RS) images practical and efficient, its large-scale application is still limited …
THE benchmark: Transferable representation learning for monocular height estimation
Generating 3-D city models rapidly is crucial for many applications. Monocular height
estimation (MHE) is one of the most efficient and timely ways to obtain large-scale geometric …
estimation (MHE) is one of the most efficient and timely ways to obtain large-scale geometric …
SyntCities: A large synthetic remote sensing dataset for disparity estimation
Studies in the last years have proved the outstanding performance of deep learning for
computer vision tasks in the remote sensing field, such as disparity estimation. However …
computer vision tasks in the remote sensing field, such as disparity estimation. However …