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README.md

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   2.3.30. [Flood-Mapping-Using-Satellite-Images](https://github.com/KonstantinosF/Flood-Mapping-Using-Satellite-Images) -> masters thesis comparing Random Forest & Unet
 
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 ### 2.4. Segmentation - Fire, smoke & burn areas
 
   2.4.1. [SatelliteVu-AWS-Disaster-Response-Hackathon](https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon) -> fire spread prediction using classical ML & deep learning `BEGINNER`
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   2.4.11. [burned-area-baseline](https://github.com/lccol/burned-area-baseline) -> baseline unet model accompanying the Satellite Burned Area Dataset (Sentinel 1 & 2)
 
   2.4.12. [burned-area-seg](https://github.com/links-ads/burned-area-seg) -> Burned area segmentation from Sentinel-2 using multi-task learning
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+  2.4.13. [chabud2023](https://github.com/developmentseed/chabud2023) -> Change detection for Burned area Delineation (ChaBuD) ECML/PKDD 2023 challenge
 
 
 ### 2.5.  Segmentation - Landslides
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   24.14. [MESSL](https://github.com/OMEGAFSL/MESSL) -> code for paper: Multiform Ensemble Self-Supervised Learning for Few-Shot Remote Sensing Scene Classification
 
+  24.15. [SCCNet](https://github.com/linhanwang/SCCNet) -> code for 2023 paper: Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation
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 ## 25. Self-supervised, unsupervised & contrastive learning
 Self-supervised, unsupervised & contrastive learning are all methods of machine learning that use unlabeled data to train algorithms. Self-supervised learning uses labeled data to create an artificial supervisor, while unsupervised learning uses only the data itself to identify patterns and similarities. Contrastive learning uses pairs of data points to learn representations of data, usually for classification tasks.