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Deep Learning-Based Grassland Classification Using Multi-Modal Sentinel-1, Sentinel-2, and Street-Level Imagery

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

inferredcites →https://doi.org/10.1002/ecs2.2582openalex-snapshot

Grasslands—more important for ecosystem services than you might think

inferredcites →https://doi.org/10.1007/978-3-642-34062-8_32openalex-snapshot

New Machine Learning Algorithm: Random Forest

inferredcites →https://doi.org/10.1007/bf01104990openalex-snapshot

Assessment of C budget for grasslands and drylands of the world

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.2022.102794openalex-snapshot

Multi-modal fusion of satellite and street-view images for urban village classification based on a dual-branch deep neural network

inferredcites →https://doi.org/10.1017/s1751731112000304openalex-snapshot

The importance of grasslands for animal production and other functions: a review on management and methodological progress in the tropics

inferredcites →https://doi.org/10.1038/s41598-022-04932-6openalex-snapshot

Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject region

inferredcites →https://doi.org/10.1073/pnas.2211613120openalex-snapshot

Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies

inferredcites →https://doi.org/10.1109/5254.708428openalex-snapshot

Support vector machines

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/mc.2010.170openalex-snapshot

Google Street View: Capturing the World at Street Level

inferredcites →https://doi.org/10.1109/mgrs.2017.2762307openalex-snapshot

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

inferredcites →https://doi.org/10.1111/nzg.12000openalex-snapshot

Remote sensing methods to detect land‐use/cover changes in N ew Z ealand's ‘indigenous’ grasslands

inferredcites →https://doi.org/10.1126/science.aaa8415openalex-snapshot

Machine learning: Trends, perspectives, and prospects

inferredcites →https://doi.org/10.1145/3468264.3468537openalex-snapshot

Bias in machine learning software: why? how? what to do?

inferredcites →https://doi.org/10.1155/2024/6671395openalex-snapshot

Classification Performance Analysis of Decision Tree‐Based Algorithms with Noisy Class Variable

inferredcites →https://doi.org/10.1161/circulationaha.106.682658openalex-snapshot

Logistic Regression

inferredcites →https://doi.org/10.3390/rs14112621openalex-snapshot

Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes

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