Remove outdated links from the techniques section in README.md

Author: robmarkcole

README.md

@@ -691,8 +691,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [FER-CNN](https://github.com/runnergirl13/FER-CNN) -> Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks
 
-- [UNET-Image-Segmentation-Satellite-Picture](https://github.com/rwie1and/UNET-Image-Segmentation-Satellite-Pictures) -> Unet to predict roof tops on Crowed AI Mapping dataset, uses keras
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 - [Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET](https://github.com/ManishSahu53/Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET) -> applied to geo-referenced images which are very large size > 10k x 10k pixels
 
 - [building-footprint-segmentation](https://github.com/fuzailpalnak/building-footprint-segmentation) -> pip installable library to train building footprint segmentation on satellite and aerial imagery, applied to Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset
@@ -709,16 +707,12 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [Boundary Enhancement Semantic Segmentation for Building Extraction](https://github.com/hin1115/BEmodule-Satellite-Building-Segmentation)
 
-- [keras code for binary semantic segmentation](https://github.com/loveswine/UNet_keras_for_RSimage)
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 - [Spacenet-Building-Detection](https://github.com/IdanC1s2/Spacenet-Building-Detection)
 
 - [LGPNet-BCD](https://github.com/TongfeiLiu/LGPNet-BCD) -> Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy
 
 - [MTL_homoscedastic_SRB](https://github.com/burakekim/MTL_homoscedastic_SRB) -> A Multi-Task Deep Learning Framework for Building Footprint Segmentation
 
-- [UNet_CNN](https://github.com/Inamdarpushkar/UNet_CNN) -> UNet model to segment building coverage in Boston using Remote sensing data, uses keras
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 - [FDANet](https://github.com/daifeng2016/FDANet) -> Full-Level Domain Adaptation for Building Extraction in Very-High-Resolution Optical Remote-Sensing Images
 
 - [CBRNet](https://github.com/HaonanGuo/CBRNet) -> A Coarse-to-fine Boundary Refinement Network for Building Extraction from Remote Sensing Imagery
@@ -832,8 +826,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [solar-panel-segmentation)](https://github.com/gabrieltseng/solar-panel-segmentation) -> Finding solar panels using USGS satellite imagery
 
-- [solar_seg](https://github.com/tcapelle/solar_seg) -> Solar segmentation of PV modules (sub elements of panels) using drone images and fast.ai
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 - [solar_plant_detection](https://github.com/Amirmoradi94/solar_plant_detection) -> boundary extraction of Photovoltaic (PV) plants using Mask RCNN and Amir dataset
 
 - [SolarDetection](https://github.com/A-Stangeland/SolarDetection) -> unet on satellite image from the USA and France
@@ -922,11 +914,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [SCAttNet](https://github.com/lehaifeng/SCAttNet) -> Semantic Segmentation Network with Spatial and Channel Attention Mechanism
 
-- [unetseg](https://github.com/dymaxionlabs/unetseg) -> A set of classes and CLI tools for training a semantic segmentation model based on the U-Net architecture, using Tensorflow and Keras. This implementation is tuned specifically for satellite imagery and other geospatial raster data
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-- [Semantic Segmentation of Satellite Imagery using U-Net & fast.ai](https://medium.com/dataseries/image-semantic-segmentation-of-satellite-imagery-using-u-net-e99ae13cf464) -> with [repo](https://github.com/raoofnaushad/Image-Semantic-Segmentation-of-Satellite-Imagery-using-U-Net.)
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-- [clusternet_segmentation](https://github.com/zhygallo/clusternet_segmentation) -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network
 
 - [Efficient-Transformer](https://github.com/zyxu1996/Efficient-Transformer) -> Efficient Transformer for Remote Sensing Image Segmentation
 
@@ -946,8 +933,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [SNDF](https://github.com/mi18/SNDF) -> Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation
 
-- [Satellite-Image-Classification](https://github.com/yxian29/Satellite-Image-Classification) -> using random forest or support vector machines (SVM) and sklearn
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 - [dynamic-rs-segmentation](https://github.com/keillernogueira/dynamic-rs-segmentation) -> Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
 
 - [segmentation_models.pytorch](https://github.com/qubvel/segmentation_models.pytorch) -> Segmentation models with pretrained backbones, has been used in multiple winning solutions to remote sensing competitions
@@ -1014,8 +999,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [MAResU-Net](https://github.com/lironui/MAResU-Net) -> Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
 
-- [ml_segmentation](https://github.com/dgriffiths3/ml_segmentation) -> semantic segmentation of buildings using Random Forest, Support Vector Machine (SVM) & Gradient Boosting Classifier (GBC)
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 - [RSEN](https://github.com/YonghaoXu/RSEN) -> Robust Self-Ensembling Network for Hyperspectral Image Classification
 
 - [MSNet](https://github.com/taochx/MSNet) -> multispectral semantic segmentation network for remote sensing images
@@ -1087,12 +1070,8 @@ In instance segmentation, each individual 'instance' of a segmented area is give
 
 - [Mask_RCNN](https://github.com/matterport/Mask_RCNN) generates bounding boxes and segmentation masks for each instance of an object in the image. It is very commonly used for instance segmentation & object detection
 
-- [Instance segmentation of center pivot irrigation system in Brazil](https://github.com/saraivaufc/instance-segmentation-maskrcnn) using free Landsat images, mask R-CNN & Keras
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 - [Building-Detection-MaskRCNN](https://github.com/Mstfakts/Building-Detection-MaskRCNN) -> Building detection from the SpaceNet dataset by using Mask RCNN
 
-- [Oil tank instance segmentation with Mask R-CNN](https://github.com/georgiosouzounis/instance-segmentation-mask-rcnn) with [accompanying article](https://medium.com/@georgios.ouzounis/oil-storage-tank-instance-segmentation-with-mask-r-cnn-77c94433045f) using Keras & Airbus Oil Storage Detection Dataset on Kaggle
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 - [Mask_RCNN-for-Caravans](https://github.com/OrdnanceSurvey/Mask_RCNN-for-Caravans) -> detect caravan footprints from OS imagery
 
 - [parking_bays_detectron2](https://github.com/spiyer99/parking_bays_detectron2) -> Detecting parking bays with satellite imagery. Used Detectron2 and synthetic data with Unreal, superior performance to using Mask RCNN
@@ -1670,9 +1649,6 @@ When the object count, but not its shape is required, U-net can be used to treat
 
 Regression in remote sensing involves predicting continuous variables such as wind speed, tree height, or soil moisture from an image. Both classical machine learning and deep learning approaches can be used to accomplish this task. Classical machine learning utilizes feature engineering to extract numerical values from the input data, which are then used as input for a regression algorithm like linear regression. On the other hand, deep learning typically employs a convolutional neural network (CNN) to process the image data, followed by a fully connected neural network (FCNN) for regression. The FCNN is trained to map the input image to the desired output, providing predictions for the continuous variables of interest. [Image source](https://github.com/h-fuzzy-logic/python-windspeed)
 
-- [python-windspeed](https://github.com/h-fuzzy-logic/python-windspeed) -> Predicting windspeed of hurricanes from satellite images, uses CNN regression in keras
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-- [hurricane-wind-speed-cnn](https://github.com/23ccozad/hurricane-wind-speed-cnn) -> Predicting windspeed of hurricanes from satellite images, uses CNN regression in keras
 
 - [GEDI-BDL](https://github.com/langnico/GEDI-BDL) -> Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
 
@@ -1725,8 +1701,6 @@ Clouds are a major issue in remote sensing images as they can obscure the underl
 
 - [sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector) -> Sentinel Hub Cloud Detector for Sentinel-2 images in Python
 
-- [dsen2-cr](https://github.com/ameraner/dsen2-cr) -> cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion, contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset
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 - [pyatsa](https://github.com/agroimpacts/pyatsa) -> Python package implementing the Automated Time-Series Analysis method for masking clouds in satellite imagery developed by Zhu and Helmer 2018
 
 - [decloud](https://github.com/CNES/decloud) -> Decloud enables the training of various deep nets to remove clouds in optical image, using e.g. Sentinel 1 & 2
@@ -1765,9 +1739,6 @@ Clouds are a major issue in remote sensing images as they can obscure the underl
 
 - [CloudX-Net](https://github.com/sumitkanu/CloudX-Net) -> an efficient and robust architecture used for detection of clouds from satellite images
 
-- [A simple cloud-detection walk-through using Convolutional Neural Network (CNN and U-Net) and fast.ai library](https://medium.com/analytics-vidhya/a-simple-cloud-detection-walk-through-using-convolutional-neural-network-cnn-and-u-net-and-bc745dda4b04)
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-- [38Cloud-Medium](https://github.com/cordmaur/38Cloud-Medium) -> Walk-through using u-net to detect clouds in satellite images with fast.ai
 
 - [cloud_detection_using_satellite_data](https://github.com/ZhouPeng-NIMST/cloud_detection_using_satellite_data) -> performed on Sentinel 2 data
 
@@ -1862,8 +1833,6 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [Unstructured-change-detection-using-CNN](https://github.com/vbhavank/Unstructured-change-detection-using-CNN)
 
-- [Siamese neural network to detect changes in aerial images](https://github.com/vbhavank/Siamese-neural-network-for-change-detection) -> uses Keras and VGG16 architecture
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 - [Change Detection in 3D: Generating Digital Elevation Models from Dove Imagery](https://www.planet.com/pulse/publications/change-detection-in-3d-generating-digital-elevation-models-from-dove-imagery/)
 
 - [QGIS plugin for applying change detection algorithms on high resolution satellite imagery](https://github.com/dymaxionlabs/massive-change-detection)
@@ -1914,9 +1883,6 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [Satellite-Image-Alignment-Differencing-and-Segmentation](https://github.com/rishi5kesh/Satellite-Image-Alignment-Differencing-and-Segmentation)
 
-- [Change Detection in Multi-temporal Satellite Images](https://github.com/IhebeddineRyahi/Change-detection-in-multitemporal-satellite-images) -> uses Principal Component Analysis (PCA) and K-means clustering
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-- [Unsupervised Change Detection Algorithm using PCA and K-Means Clustering](https://github.com/leduckhai/Change-Detection-PCA-KMeans) -> in Matlab but has paper
 
 - [ChangeFormer](https://github.com/wgcban/ChangeFormer) -> A Transformer-Based Siamese Network for Change Detection. Uses transformer architecture to address the limitations of CNN in handling multi-scale long-range details. Demonstrates that ChangeFormer captures much finer details compared to the other SOTA methods, achieving better performance on benchmark datasets
 
@@ -1958,8 +1924,6 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [LSNet](https://github.com/qaz670756/LSNet) ->  Extremely Light-Weight Siamese Network For Change Detection in Remote Sensing Image
 
-- [Change-Detection-in-Remote-Sensing-Images](https://github.com/themrityunjay/Change-Detection-in-Remote-Sensing-Images) ->  using PCA & K-means
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 - [End-to-end-CD-for-VHR-satellite-image](https://github.com/daifeng2016/End-to-end-CD-for-VHR-satellite-image) -> End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
 
 - [Semantic-Change-Detection](https://github.com/daifeng2016/Semantic-Change-Detection) -> SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery
@@ -2010,8 +1974,6 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [CorrFusionNet](https://github.com/rulixiang/CorrFusionNet) -> Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
 
-- [ChangeDetectionPCAKmeans](https://github.com/rulixiang/ChangeDetectionPCAKmeans) -> Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering.
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 - [IRCNN](https://github.com/thebinyang/IRCNN) -> IRCNN: An Irregular-Time-Distanced Recurrent Convolutional Neural Network for Change Detection in Satellite Time Series
 
 - [UTRNet](https://github.com/thebinyang/UTRNet) -> An Unsupervised Time-Distance-Guided Convolutional Recurrent Network for Change Detection in Irregularly Collected Images
@@ -2046,8 +2008,6 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [BGAAE_CD](https://github.com/xauter/BGAAE_CD) -> Bipartite Graph Attention Autoencoders for Unsupervised Change Detection Using VHR Remote Sensing Images
 
-- [Unsupervised-Change-Detection](https://github.com/voodooed/Unsupervised-Change-Detection) -> Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering
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 - [Metric-CD](https://github.com/wgcban/Metric-CD) -> Deep Metric Learning for Unsupervised Change Detection in Remote Sensing Images
 
 - [HANet-CD](https://github.com/ChengxiHAN/HANet-CD) -> HANet: A hierarchical attention network for change detection with bi-temporal very-high-resolution remote sensing images