Deep learning for check dam area extraction with optical images and digital elevation model: A case study in the hilly and gully regions of the Loess Plateau, China
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning for check dam area extraction with optical images and digital elevation model: A case study in the hilly and gully regions of the Loess Plateau, China
Authors
Keywords
-
Journal
EARTH SURFACE PROCESSES AND LANDFORMS
Volume -, Issue -, Pages -
Publisher
Wiley
Online
2023-06-28
DOI
10.1002/esp.5652
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Dynamic Modeling Framework of Sediment Trapped by Check-Dam Networks: A Case Study of a Typical Watershed on the Chinese Loess Plateau
- (2022) Pengcheng Sun et al. Engineering
- Using Open Vector-Based Spatial Data to Create Semantic Datasets for Building Segmentation for Raster Data
- (2022) Szymon Glinka et al. Remote Sensing
- Deforestation Detection in the Amazon Using DeepLabv3+ Semantic Segmentation Model Variants
- (2022) Renan Bides de Andrade et al. Remote Sensing
- Extracting check dam areas from high‐resolution imagery based on the integration of object‐based image analysis and deep learning
- (2021) Sijin Li et al. LAND DEGRADATION & DEVELOPMENT
- Potato crop stress identification in aerial images using deep learning‐based object detection
- (2021) Sujata Butte et al. AGRONOMY JOURNAL
- Landslide susceptibility prediction based on image semantic segmentation
- (2021) Bowen Du et al. COMPUTERS & GEOSCIENCES
- An extreme May 2018 debris flood case study in northern Slovenia: analysis, modelling, and mitigation
- (2020) Nejc Bezak et al. Landslides
- Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models
- (2020) Nikhil Prakash et al. Remote Sensing
- Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends
- (2020) Thorsten Hoeser et al. Remote Sensing
- Soil erosion and sediment interception by check dams in a watershed for an extreme rainstorm on the Loess Plateau, China
- (2020) Leichao Bai et al. International Journal of Sediment Research
- Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning
- (2020) Remis Balaniuk et al. SENSORS
- Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
- (2019) Omid Ghorbanzadeh et al. Remote Sensing
- Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery
- (2019) Nicholus Mboga et al. Remote Sensing
- Land-use changes and check dams reducing runoff and sediment yield on the Loess Plateau of China
- (2019) Peng Shi et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Effects of DEM resolution on the accuracy of gully maps in loess hilly areas
- (2019) Wen Dai et al. CATENA
- sUAS, SfM-MVS photogrammetry and a topographic algorithm method to quantify the volume of sediments retained in check-dams
- (2019) Alberto Alfonso-Torreño et al. SCIENCE OF THE TOTAL ENVIRONMENT
- The use of check dams in watershed management projects: Examples from around the world
- (2019) Naseer Ahmed Abbasi et al. SCIENCE OF THE TOTAL ENVIRONMENT
- A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning
- (2019) Fanxuan Zeng et al. Remote Sensing
- A survey on deep learning techniques for image and video semantic segmentation
- (2018) Alberto Garcia-Garcia et al. APPLIED SOFT COMPUTING
- Variation in the sediment deposition behind check-dams under different soil erosion conditions on the Loess Plateau, China
- (2018) Yanhong Wei et al. EARTH SURFACE PROCESSES AND LANDFORMS
- Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States
- (2018) Hsiuhan Lexie Yang et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Automated regolith landform mapping using airborne geophysics and remote sensing data, Burkina Faso, West Africa
- (2018) Václav Metelka et al. REMOTE SENSING OF ENVIRONMENT
- Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module
- (2018) Bo Yu et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- The role of check dams in retaining organic carbon and nutrients. A study case in the Sierra de Ávila mountain range (Central Spain)
- (2018) J. Mongil-Manso et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Effects of check dams on bed-load transport and steep-slope stream morphodynamics
- (2017) Guillaume Piton et al. GEOMORPHOLOGY
- Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images
- (2017) Claudio Persello et al. IEEE Geoscience and Remote Sensing Letters
- Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
- (2017) John E. Ball et al. Journal of Applied Remote Sensing
- Automatic building footprint extraction from high-resolution satellite image using mathematical morphology
- (2017) Nitin L. Gavankar et al. European Journal of Remote Sensing
- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
- (2016) Liangpei Zhang et al. IEEE Geoscience and Remote Sensing Magazine
- The correlation analysis on the landscape pattern index and hydrological processes in the Yanhe watershed, China
- (2015) Z.X. Zhou et al. JOURNAL OF HYDROLOGY
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Check dam identification using multisource data and their effects on streamflow and sediment load in a Chinese Loess Plateau catchment
- (2013) Peng Tian et al. Journal of Applied Remote Sensing
- How Many Check Dams Do We Need To Build on the Loess Plateau?
- (2012) Zhao Jin et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
- Check Dam in the Loess Plateau of China: Engineering for Environmental Services and Food Security.
- (2011) Yafeng Wang et al. ENVIRONMENTAL SCIENCE & TECHNOLOGY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started