On the public citation graph

Analysis of Ecosystem Classification and Pattern Evolution in Shengjin Lake National Nature Reserve Based on Dsanet

https://doi.org/10.2139/ssrn.5363884
0 signed · 39 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s10457-007-9055-8openalex-snapshot
Differences in land cover interpretation in landscapes rich in cover gradients: reflections based on the montado of South Portugal
inferredcites →https://doi.org/10.1007/s10489-022-03345-2openalex-snapshot
Lake water body extraction of optical remote sensing images based on semantic segmentation
inferredcites →https://doi.org/10.1007/s10661-019-7314-6openalex-snapshot
Quantifying the extent of human disturbance activities and anthropogenic stressors in wetlands across the conterminous United States: results from the National Wetland Condition Assessment
inferredcites →https://doi.org/10.1007/s12524-019-01056-9openalex-snapshot
Comparing Support Vector Machines and Maximum Likelihood Classifiers for Mapping of Urbanization
inferredcites →https://doi.org/10.1007/s13369-022-06734-4openalex-snapshot
Semantic Segmentation of Satellite Images Using Deep-Unet
inferredcites →https://doi.org/10.1016/j.isprsjprs.2016.01.011openalex-snapshot
Random forest in remote sensing: A review of applications and future directions
inferredcites →https://doi.org/10.1016/j.jag.2024.104093openalex-snapshot
DDPM-SegFormer: Highly refined feature land use and land cover segmentation with a fused denoising diffusion probabilistic model and transformer
inferredcites →https://doi.org/10.1016/j.patcog.2024.110976openalex-snapshot
TBNet: A texture and boundary-aware network for small weak object detection in remote-sensing imagery
inferredcites →https://doi.org/10.1016/j.scitotenv.2023.163112openalex-snapshot
Influence of human activities and climate change on wetland landscape pattern—A review
inferredcites →https://doi.org/10.1080/01431160600675911openalex-snapshot
Land cover in East Asia classified using Terra MODIS and DMSP OLS products
inferredcites →https://doi.org/10.1080/01431161.2018.1553320openalex-snapshot
Annual land-cover mapping based on multi-temporal cloud-contaminated landsat images
inferredcites →https://doi.org/10.1080/15481603.2022.2152926openalex-snapshot
A framework for fine classification of urban wetlands based on random forest and knowledge rules: taking the wetland cities of Haikou and Yinchuan as examples
inferredcites →https://doi.org/10.1080/17538947.2025.2496790openalex-snapshot
Semantic enhancement and change consistency network for semantic change detection in remote sensing images
inferredcites →https://doi.org/10.1109/jstars.2013.2272212openalex-snapshot
Urban Land Cover Classification With Airborne Hyperspectral Data: What Features to Use?
inferredcites →https://doi.org/10.1109/jstars.2019.2918242openalex-snapshot
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
inferredcites →https://doi.org/10.1109/jstars.2024.3360458openalex-snapshot
Robust Land Cover Classification With Local–Global Information Decoupling to Address Remote Sensing Anomalous Data
inferredcites →https://doi.org/10.1109/jstars.2024.3394571openalex-snapshot
Dual-Dimension Feature Interaction for Semantic Change Detection in Remote Sensing Images
inferredcites →https://doi.org/10.1109/jstars.2024.3422901openalex-snapshot
MSSM-SCDNet: A Multiclass Semantic Change Detection Network Suitable for Coastal Areas Based on Multiband Spatial-spectral Attention Mechanism
inferredcites →https://doi.org/10.1109/jstars.2025.3554272openalex-snapshot
Multitask Change-Aware Network and Semisupervised Enhanced Multistep Training for Semantic Change Detection
inferredcites →https://doi.org/10.1109/lgrs.2018.2803259openalex-snapshot
Very High Resolution Object-Based Land Use–Land Cover Urban Classification Using Extreme Gradient Boosting
inferredcites →https://doi.org/10.1109/lgrs.2024.3385404openalex-snapshot
TBSCD-Net: A Siamese Multitask Network Integrating Transformers and Boundary Regularization for Semantic Change Detection From VHR Satellite Images
inferredcites →https://doi.org/10.1109/tgrs.2023.3283508openalex-snapshot
Multimodal Transformer Network for Hyperspectral and LiDAR Classification
inferredcites →https://doi.org/10.1109/tgrs.2024.3362795openalex-snapshot
Joint Spatio-Temporal Modeling for Semantic Change Detection in Remote Sensing Images
inferredcites →https://doi.org/10.1109/tgrs.2025.3558472openalex-snapshot
Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation
inferredcites →https://doi.org/10.1109/tgrs.2025.3585238openalex-snapshot
A Unified Framework With Multimodal Fine-Tuning for Remote Sensing Semantic Segmentation
inferredcites →https://doi.org/10.1117/1.jrs.13.014521openalex-snapshot
Combining random forest and support vector machines for object-based rural-land-cover classification using high spatial resolution imagery
inferredcites →https://doi.org/10.1126/science.269.5222.331openalex-snapshot
Landscape Ecology: Spatial Heterogeneity in Ecological Systems
inferredcites →https://doi.org/10.11834/jrs.20210164openalex-snapshot
Machine learning paradigms in high-resolution remote sensing image interpretation
inferredcites →https://doi.org/10.11834/jrs.20210382openalex-snapshot
Research progress and trend of high-resolution remote sensing imagery intelligent interpretation
inferredcites →https://doi.org/10.18494/sam.2021.3612openalex-snapshot
Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data
inferredcites →https://doi.org/10.3390/ijerph16101818openalex-snapshot
Wetlands of International Importance: Status, Threats, and Future Protection
inferredcites →https://doi.org/10.3390/ijgi9080478openalex-snapshot
Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images
inferredcites →https://doi.org/10.3390/rs13142709openalex-snapshot
Gradient Boosting Machine and Object-Based CNN for Land Cover Classification
inferredcites →https://doi.org/10.3390/rs15051275openalex-snapshot
Contribution of Land Cover Classification Results Based on Sentinel-1 and 2 to the Accreditation of Wetland Cities
inferredcites →https://doi.org/10.3390/rs15082027openalex-snapshot
Efficient Deep Semantic Segmentation for Land Cover Classification Using Sentinel Imagery
inferredcites →https://doi.org/10.3390/rs6043369openalex-snapshot
Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification
inferredcites →https://doi.org/10.48550/arxiv.2405.10530openalex-snapshot
CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
inferredcites →https://doi.org/10.5281/zenodo.7711096openalex-snapshot
EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.