Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Enping Yan
Bridging Domains and Resolutions: Deep Learning-Based Land Cover Mapping without Matched Labels
1. Introduction Land cover mapping plays an instrumental role in depicting the Earth’s surface characteristics, essential for environmental monitoring and management [1,2]. The dynamic interplay of policy shifts and natural events continuously alters land cover, necessitating regular updates to maintain the credibility of these maps [3,4]. Historically, land cover mapping relied heavily on manual delineation, a process fraught with inefficiencies [5].
A Cross-Domain Change Detection Network Based on Instance Normalization
1. Introduction Convolutional neural networks (CNNs) based on deep learning have become a valuable tool in computer vision [1], enabling tasks such as image recognition [2], object detection [3,4,5], semantic segmentation [6,7,8], image super-resolution [9], and change detection [,]. Change detection is a crucial technique in remote sensing image analysis and is extensively used in land resource planning, forest resource monitoring [,], and natural disaster monitoring and assessment [].
Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Investigating the Identification and Spatial Distribution Characteristics of Camellia oleifera Plantations Using High-Resolution Imagery
3. Results 3.1. Analysis of Model Training and Validation Efficiency As illustrated in , we present the loss and validation set mIOU curves for four distinct network segmentation models—U-Net++, U-Net, Deeplabv3+, and Linknet—over the course of training with an identical sample set. It is evident from the training set that the convergence speed and ultimate loss level of all four models closely align.
Dynamic Detection of Forest Change in Hunan Province Based on Sentinel-2 Images and Deep Learning
Abstract : Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Enping
Contact Enping, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack