Update README.md

README.md

@@ -553,7 +553,7 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 ### 2.5.  Segmentation - Landslides
 
-  2.5.1. [landslide4sense](https://www.iarai.ac.at/landslide4sense/) -> a competition focused on landslide detection using globally distributed multi-source satellite imagery. [Baseline solution unet](https://github.com/isaaccorley/landslide4sense)  `BEGINNER`
+  2.5.1. [landslide-sar-unet](https://github.com/iprapas/landslide-sar-unet) -> code for 2022 [paper](https://arxiv.org/abs/2211.02869): Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
 
   2.5.2. [landslide-mapping-with-cnn](https://github.com/nprksh/landslide-mapping-with-cnn) -> code for 2021 [paper](https://www.nature.com/articles/s41598-021-89015-8): A new strategy to map landslides with a generalized convolutional neural network
 
@@ -563,8 +563,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
   2.5.5. [SAR-landslide-detection-pretraining](https://github.com/VMBoehm/SAR-landslide-detection-pretraining) -> code for the 2022 [paper](https://arxiv.org/abs/2211.09927): SAR-based landslide classification pretraining leads to better segmentation
 
-  2.5.6. [landslide-sar-unet](https://github.com/iprapas/landslide-sar-unet) -> code for 2022 [paper](https://arxiv.org/abs/2211.02869): Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
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 ### 2.6. Segmentation - Glaciers
 
   2.6.1. [HED-UNet](https://github.com/khdlr/HED-UNet) -> a model for simultaneous semantic segmentation and edge detection, examples provided are glacier fronts and building footprints using the Inria Aerial Image Labeling dataset
@@ -640,7 +638,7 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
   2.8.19. [road_building_extraction](https://github.com/jeffwen/road_building_extraction) -> Pytorch implementation of U-Net architecture for road and building extraction
 
-  2.8.20. [Satellite-Imagery-Road-Extraction](https://github.com/Akash-Ramjyothi/Satellite-Imagery-Road-Extraction) -> research project in keras
+  2.8.20. [RCFSNet](https://github.com/CVer-Yang/RCFSNet) -> code for 2022 paper: Road Extraction From Satellite Imagery by Road Context and Full-Stage Feature
 
   2.8.21. [SGCN](https://github.com/tist0bsc/SGCN) -> code for 2021 [paper](https://ieeexplore.ieee.org/document/9614130): Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images
 
@@ -688,8 +686,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
   2.8.43. [tile2net](https://github.com/VIDA-NYU/tile2net) -> code for 2023 paper: Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery
 
-  2.8.44. [RCFSNet](https://github.com/CVer-Yang/RCFSNet) -> code for 2022 paper: Road Extraction From Satellite Imagery by Road Context and Full-Stage Feature
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 ### 2.9. Segmentation - Buildings & rooftops
 
   2.9.1. [Road and Building Semantic Segmentation in Satellite Imagery](https://github.com/Paulymorphous/Road-Segmentation) uses U-Net on the Massachusetts Roads Dataset & keras `BEGINNER`