The Semantic Segmentation Of Remote Sensing Imagery
9 papers with code • 2 benchmarks • 5 datasets
GID
MSAW
SAMRS
Five-Billion-Pixels
UV6K
Most implemented papers
Lake Ice Monitoring with Webcams and Crowd-Sourced Images
czarmanu/deeplab-lakeice-webcams
•
•
On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
However, general semantic segmentation methods mainly focus on scale variation in the natural scene, with inadequate consideration of the other two problems that usually happen in the large area earth observation scene.
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
•
•
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.
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
•
•
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.
Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
x-ytong/DPA
•
•
To 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 five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images.
DeepMAO: Deep Multi-scale Aware Overcomplete Network for Building Segmentation in Satellite Imagery
Sumanth181099/DeepMAO
•
•
Computer Vision and Pattern Recognition, Perception Beyond Visible Spectrum Workshop 2023
Experimental results on SpaceNet 6 dataset, on both EO and SAR modalities, and the INRIA dataset show that DeepMAO achieves state-of-the-art building segmentation performance, including small and complex-shaped buildings with a negligible increase in the parameter count.
