Browse State-of-the-Art › Segmentation Of Remote Sensing Imagery
Segmentation Of Remote Sensing Imagery
15 papers with code · 0 benchmarks · 3 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
3 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
1 subtask in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
15 shown of 15 papers with code (27 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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28 Jun 2023 3 repositories listedLand cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management.
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16 Jun 2024 2 repositories listedSemantic segmentation, as a basic tool for intelligent interpretation of remote sensing images, plays a vital role in many Earth Observation (EO) applications.
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5 Dec 2023 2 repositories listed Syntology ran 0 of 13 samples · 13 unverifiedFurthermore, the boundary loss capitalizes on the distinctive features of SGB by directing the model's attention to the boundary information of the object.
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18 Feb 2020 2 repositories listedOn average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
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24 Jun 2024 1 repository listedIn particular, we introduce affine transformations in the LCA module for adaptive extraction of local class representations to effectively tolerate scale and orientation variations in remotely sensed images.
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30 May 2024 1 repository listedThis study introduces FMARS (Foundation Model Annotations in Remote Sensing), a methodology leveraging VHR imagery and foundation models for fast and robust annotation.
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20 Apr 2023 1 repository listed Syntology ran 0 of 4 samples · 4 unverifiedAs image databases grow each year, performing automatic segmentation with deep learning models has gradually become the standard approach for processing the data.
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1 Sep 2022 1 repository listed Syntology ran 0 of 7 samples · 7 unverifiedTo validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other…
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11 Feb 2021 1 repository listedTo circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds.
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18 Feb 2020 1 repository listedOn average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
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17 Feb 2020 1 repository listedLake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming.
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28 Oct 2018 1 repository listed Syntology ran 0 of 3 samples · 3 unverifiedClouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods.
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28 May 2018 1 repository listedContinuous monitoring of climate indicators is important for understanding the dynamics and trends of the climate system.
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1 Apr 2018 1 repository listedDeep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.
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26 Mar 2018 1 repository listedThese low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery.
Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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