Remove outdated links from segmentation and road detection sections in README.md

Author: robmarkcole

README.md

@@ -340,7 +340,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 ### Segmentation - Water, coastlines, rivers & floods
 
-- [pytorch-waterbody-segmentation](https://github.com/gauthamk02/pytorch-waterbody-segmentation) -> UNET model trained on the Satellite Images of Water Bodies dataset from Kaggle. The model is deployed on Hugging Face Spaces
 
 
 
@@ -349,7 +348,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 - [ml4floods](https://github.com/spaceml-org/ml4floods) -> An ecosystem of data, models and code pipelines to tackle flooding with ML
 
 
-- [Map Floodwater of SAR Imagery with SageMaker](https://github.com/JayThibs/map-floodwater-sar-imagery-on-sagemaker) -> applied to Sentinel-1 dataset
 
 - [1st place solution for STAC Overflow: Map Floodwater from Radar Imagery hosted by Microsoft AI for Earth](https://github.com/sweetlhare/STAC-Overflow) -> combines Unet with Catboostclassifier, taking their maxima, not the average
 
@@ -358,11 +356,8 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 - [CoastSat](https://github.com/kvos/CoastSat) -> tool for mapping coastlines which has an extension [CoastSeg](https://github.com/dbuscombe-usgs/CoastSeg) using segmentation models
 
 
-- [Flood Event Detection Utilizing Satellite Images](https://github.com/KonstantinosF/Flood-Detection---Satellite-Images)
 
-- [ETCI-2021-Competition-on-Flood-Detection](https://github.com/sidgan/ETCI-2021-Competition-on-Flood-Detection) -> Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training
 
-- [FDSI](https://github.com/keillernogueira/FDSI) -> Flood Detection in Satellite Images - 2017 Multimedia Satellite Task
 
 - [deepwatermap](https://github.com/isikdogan/deepwatermap) -> a deep model that segments water on multispectral images
 
@@ -499,7 +494,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [Winning Solutions from SpaceNet Road Detection and Routing Challenge](https://github.com/SpaceNetChallenge/RoadDetector)
 
-- [Detecting road and road types jupyter notebook](https://github.com/taspinar/sidl/blob/master/notebooks/2_Detecting_road_and_roadtypes_in_sattelite_images.ipynb)
 
 - [awesome-deep-map](https://github.com/antran89/awesome-deep-map) -> A curated list of resources dedicated to deep learning / computer vision algorithms for mapping. The mapping problems include road network inference, building footprint extraction, etc.