Remove outdated references and links from the segmentation section in README.md

Author: robmarkcole

README.md

@@ -163,7 +163,6 @@ Image segmentation is a crucial step in image analysis and computer vision, with
 
 - [laika](https://github.com/datasciencecampus/laika) -> The goal of this repo is to research potential sources of satellite image data and to implement various algorithms for satellite image segmentation
 
-- landcover dot io -> was a human-in-the-loop AI tool to drastically reduce the time required to produce an accurate Land Use/Land Cover (LULC) map, [blog post](http://devseed.com/blog/2021-05-17-pearl-ai-land-cover), used Microsoft Planetary Computer and ML models run locally in the browser. Code for [backelnd](https://github.com/developmentseed/pearl-backend) and [frontend](https://github.com/developmentseed/pearl-frontend)
 
 - [CDL-Segmentation](https://github.com/asimniazi63/CDL-Segmentation) -> Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study. Compares UNet, SegNet & DeepLabv3+
 
@@ -178,7 +177,6 @@ Image segmentation is a crucial step in image analysis and computer vision, with
 
 - [MCANet](https://github.com/yisun98/SOLC) -> A joint semantic segmentation framework of optical and SAR images for land use classification. Uses [WHU-OPT-SAR-dataset](https://github.com/AmberHen/WHU-OPT-SAR-dataset)
 
-- [MUnet-LUC](https://github.com/abhi170599/MUnet-LUC)
 
 - [land-cover](https://github.com/lucashu1/land-cover) -> Model Generalization in Deep Learning Applications for Land Cover Mapping
 
@@ -189,7 +187,6 @@ Image segmentation is a crucial step in image analysis and computer vision, with
 - [SSLTransformerRS](https://github.com/HSG-AIML/SSLTransformerRS) -> Self-supervised Vision Transformers for Land-cover Segmentation and
   Classification
 
-- [aerial-tile-segmentation](https://github.com/mrsebai/aerial-tile-segmentation) -> Large satellite image semantic segmentation into 6 classes using Tensorflow 2.0 and ISPRS benchmark dataset
 
 - [LULCMapping-WV3images-CORINE-DLMethods](https://github.com/burakekim/LULCMapping-WV3images-CORINE-DLMethods) -> Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
 
@@ -211,7 +208,6 @@ Image segmentation is a crucial step in image analysis and computer vision, with
 
 - [M3SPADA](https://github.com/ecapliez/M3SPADA) ->  Multi-Sensor Temporal Unsupervised Domain Adaptation for Land Cover Mapping with spatial pseudo labelling and adversarial learning
 
-- [mitmul/ssai-cnn](https://github.com/mitmul/ssai-cnn) -> Semantic Segmentation for Aerial / Satellite Images with CNN, includes implementation of FCN for dense labeling of aerial imagery
 
 - [GLNet](https://github.com/VITA-Group/GLNet) -> Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
 
@@ -229,13 +225,11 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [DetecTree](https://github.com/martibosch/detectree) -> Tree detection from aerial imagery in Python, a LightGBM classifier of tree/non-tree pixels from aerial imagery
 
-- [Сrор field boundary detection: approaches and main challenges](https://medium.com/geekculture/%D1%81r%D0%BE%D1%80-field-boundary-detection-approaches-and-main-challenges-46e37dd276bc) -> Medium article, covering historical and modern approaches
 
 - [kenya-crop-mask](https://github.com/nasaharvest/kenya-crop-mask) -> Annual and in-season crop mapping in Kenya - LSTM classifier to classify pixels as containing crop or not, and a multi-spectral forecaster that provides a 12 month time series given a partial input. Dataset downloaded from GEE and pytorch lightning used for training
 
 - [Tree species classification from from airborne LiDAR and hyperspectral data using 3D convolutional neural networks](https://github.com/jaeeolma/tree-detection-evo)
 
-- [crop-type-classification](https://medium.com/nerd-for-tech/crop-type-classification-cf5cc2593396) -> using Sentinel 1 & 2 data with a U-Net + LSTM, more features (i.e. bands) and higher resolution produced better results (article, no code)
 
 - [Find sports fields using Mask R-CNN and overlay on open-street-map](https://github.com/jremillard/images-to-osm)
 
@@ -273,7 +267,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [crop-type-detection-ICLR-2020](https://github.com/RadiantMLHub/crop-type-detection-ICLR-2020) -> Winning Solutions from Crop Type Detection Competition at CV4A workshop, ICLR 2020
 
-- [Crop identification using satellite imagery](https://write.agrevolution.in/crop-identification-using-satellite-imagery-introduction-83d79344f9ee) -> Medium article, introduction to crop identification
 
 - [S4A-Models](https://github.com/Orion-AI-Lab/S4A-Models) -> Various experiments on the Sen4AgriNet dataset
 
@@ -286,7 +279,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 - [Official repository for the "Identifying trees on satellite images" challenge from Omdena](https://github.com/cienciaydatos/ai-challenge-trees)
 
 
-- [TreeDetection](https://github.com/AmirNiaraki/TreeDetection) -> A color-based classifier to detect the trees in google image data along with tree visual localization and crown size calculations via OpenCV
 
 - [PTDM](https://github.com/hr8yhtzb/PTDM) -> Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion
 
@@ -298,7 +290,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [kbrodt biomassters solution](https://github.com/kbrodt/biomassters) -> 1st place solution
 
-- [quqixun biomassters solution](https://github.com/quqixun/BioMassters)
 
 - [biomass-estimation](https://github.com/azavea/biomass-estimation) -> from Azavea, applied to Sentinel 1 & 2
 
@@ -318,7 +309,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [cvpr-multiearth-deforestation-segmentation](https://github.com/h2oai/cvpr-multiearth-deforestation-segmentation) -> multimodal Unet entry to the CVPR Multiearth 2023 deforestation challenge
 
-- [supervised-wheat-classification-using-pytorchs-torchgeo](https://medium.com/@sulemanhamdani10/supervised-wheat-classification-using-pytorchs-torchgeo-combining-satellite-imagery-and-python-fc7f95c82e) -> supervised wheat classification using torchgeo
 
 - [TransUNetplus2](https://github.com/aj1365/TransUNetplus2) -> TransU-Net++: Rethinking attention gated TransU-Net for deforestation mapping. Uses the Amazon and Atlantic forest dataset
 
@@ -352,15 +342,12 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [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
 
-- [Automatic Flood Detection from Satellite Images Using Deep Learning](https://medium.com/@omercaliskan99/automatic-flood-detection-from-satellite-images-using-deep-learning-f14fafd369e0)
 
-- [Semi-Supervised Classification and Segmentation on High Resolution Aerial Images - Solving the FloodNet problem](https://sahilkhose.medium.com/paper-presentation-e9bd0f3fb0bf)
 
 - [Houston_flooding](https://github.com/Lichtphyz/Houston_flooding) -> labeling each pixel as either flooded or not using data from Hurricane Harvey. Dataset consisted of pre and post flood images, and a ground truth floodwater mask was created using unsupervised clustering (with DBScan) of image pixels with human cluster verification/adjustment
 
 - [ml4floods](https://github.com/spaceml-org/ml4floods) -> An ecosystem of data, models and code pipelines to tackle flooding with ML
 
-- [A comprehensive guide to getting started with the ETCI Flood Detection competition](https://medium.com/cloud-to-street/jumpstart-your-machine-learning-satellite-competition-submission-2443b40d0a5a) -> using Sentinel1 SAR & pytorch
 
 - [Map Floodwater of SAR Imagery with SageMaker](https://github.com/JayThibs/map-floodwater-sar-imagery-on-sagemaker) -> applied to Sentinel-1 dataset
 
@@ -370,7 +357,6 @@ 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
 
-- [Satellite_Flood_Segmentation_of_Harvey](https://github.com/morgan-tam/Satellite_Flood_Segmentation_of_Harvey) -> explores both deep learning and traditional kmeans
 
 - [Flood Event Detection Utilizing Satellite Images](https://github.com/KonstantinosF/Flood-Detection---Satellite-Images)
 
@@ -2426,11 +2412,8 @@ Note that nearly all the MISR publications resulted from the [PROBA-V Super Reso
 
 - [SatelliteSR](https://github.com/kmalhan/SatelliteSR) -> comparison of a number of techniques on the DOTA dataset
 
-- [Image-Super-Resolution](https://github.com/Elangoraj/Image-Super-Resolution) -> Super resolution RESNET network
 
-- [Unsupervised Super Resolution for Sentinel-2 satellite imagery](https://github.com/savassif/Thesis) -> using Deep Image Prior (DIP), Zero-Shot Super Resolution (ΖSSR) & Degradation-Aware Super Resolution (DASR)
 
-- [Spectral Super-Resolution of Satellite Imagery with Generative Adversarial Networks](https://github.com/ImDanielRojas/thesis)
 
 - [Super resolution using GAN / 4x Improvement](https://github.com/purijs/satellite-superresolution) -> applied to Sentinel 2