Segmentation Of Remote Sensing Imagery
15 papers with code • 0 benchmarks • 3 datasets
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GID
Five-Billion-Pixels
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Most implemented papers
Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery
Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management.
Lake Ice Monitoring with Webcams and Crowd-Sourced Images
czarmanu/deeplab-lakeice-webcams
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On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
sstary/ssrs
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Furthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.
PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery
Semantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications.
Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery
These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery.
EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery
Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i. e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i. e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success.
LAKE ICE MONITORING WITH WEBCAMS
czarmanu/tiramisu_keras
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ISPRS annals of the photogrammetry, remote sensing and spatial information sciences 2018
Continuous monitoring of climate indicators is important for understanding the dynamics and trends of the climate system.
Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery
TUM-LMF/MTLCC
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Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods.
Lake Ice Detection from Sentinel-1 SAR with Deep Learning
Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming.
Photi-LakeIce Dataset
czarmanu/photi-lakeice-dataset
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ISPRS Congress 2020
On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
