The Semantic Segmentation Of Remote Sensing Imagery

6 papers with code • 0 benchmarks • 1 datasets

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Datasets


Most implemented papers

Lake Ice Monitoring with Webcams and Crowd-Sourced Images

czarmanu/deeplab-lakeice-webcams 18 Feb 2020

On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work.

Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery

rmkemker/EarthMapper 26 Mar 2018

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

rmkemker/EarthMapper 1 Apr 2018

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

czarmanu/sentinel_lakeice 17 Feb 2020

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.