Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia-Reference-Cited by-同舟云学术

Effectiveness of machine learning methods for water segmentation with ROI as the label: A case study of the Tuul River in Mongolia

Published:2021-12 Issue: Volume:103 Page:102497
ISSN:0303-2434
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Li Kai,Wang Juanle,Yao Jinyi

Funder

China Knowledge Centre for Engineering Sciences and Technology

National Natural Science Foundation of China

Chinese Academy of Sciences

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference57 articles.

1. Liu, F.; Li, G.; Wang, S.; Fu, H., Lake Water Environment Protection and Management in Mongolia. Journal of Environmental Management College of China 2015, 25 (06), 44-47+74.

2. Estimation of water requirements by livestock in Europe;Mubareka;Ecosystem Services,2013

3. The Livestock-Water Nexus Under Mixed Crop-Livestock Production System;Mekete;The Livestock-Water Nexus Under Mixed Crop-Livestock Production System,2013

4. Observations on Residents' Risk Awareness and Practice of Countermeasures against Natural Disasters in Mongolia: Questionnaire Survey Data of Khovd Citizens;Nara;Procedia Comput Sci,2019

5. Visualization Analysis for Global Water Resources Based on Digital Earth;Qu;J Coastal Res,2020

Cited by 17 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Two‐Step Approach to Extracting Sandy Beaches Through Integrating Spatial Semantic Information From Open‐Source Geospatial Datasets;Transactions in GIS;2024-09-05

2. Historic Flood Events and Current Flood Hazard in Ulaanbaatar City, Central Mongolia;Journal of Disaster Research;2024-08-01

3. Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery;International Journal of Scientific Research in Science, Engineering and Technology;2024-04-22

4. A multi-source data fusion method for land cover production: a case study of the East European Plain;International Journal of Digital Earth;2024-04-05

5. AEDNet: An Attention-Based Encoder–Decoder Network for Urban Water Extraction From High Spatial Resolution Remote Sensing Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024