S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images-Reference-Cited by-同舟云学术

S1S2-Water: A Global Dataset for Semantic Segmentation of Water Bodies From Sentinel- 1 and Sentinel-2 Satellite Images

Published:2024 Issue: Volume:17 Page:1084-1099
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wieland Marc¹^ORCID,Fichtner Florian¹^ORCID,Martinis Sandro¹^ORCID,Groth Sandro¹^ORCID,Krullikowski Christian¹^ORCID,Plank Simon¹^ORCID,Motagh Mahdi¹^ORCID

Affiliation:

1. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Oberpfaffenhofen, Germany

Funder

German Federal Ministry of Education and Research

Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungs-unterstützung im Katastrophenfall

Helmholtz Artificial Intelligence Cooperation Unit

AI for Near Real Time Satellite-Based Flood Response

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10321672.pdf?arnumber=10321672

Reference45 articles.

1. A fully automated TerraSAR-X based flood service

2. Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network

3. Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

4. Deep learning methods for flood mapping: a review of existing applications and future research directions

5. Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches

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