fixes

README.md

@@ -868,7 +868,7 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 ### Segmentation - Solar panels
 
-- [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in ..  Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)
+- [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [Dataset on kaggle](https://www.kaggle.com/datasets/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in ..  Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)
 
 - [hyperion_solar_net](https://github.com/fvergaracontesse/hyperion_solar_net) -> trained classificaton & segmentation models on RGB imagery from Google Maps
 
@@ -918,8 +918,6 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [electrical_substation_detection](https://github.com/thisishardik/electrical_substation_detection)
 
-- [PLGAN-for-Power-Line-Segmentation](https://github.com/R3ab/PLGAN-for-Power-Line-Segmentation) -> Generative Adversarial Networks for Power-Line Segmentation in Aerial Images
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 - [MCAN-OilSpillDetection](https://github.com/liyongqingupc/MCAN-OilSpillDetection) -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training
 
 - [plastics](https://github.com/earthrise-media/plastics) -> Detecting and Monitoring Plastic Waste Aggregations in Sentinel-2 Imagery for [globalplasticwatch.org](https://globalplasticwatch.org/)
@@ -1796,8 +1794,6 @@ Clouds are a major issue in remote sensing images as they can obscure the underl
 
 - [CloudXNet](https://github.com/shyamfec/CloudXNet) -> CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images
 
-- [refined-unet-lite](https://github.com/92xianshen/refined-unet-lite) -> Refined UNet Lite: End-to-End Lightweight Network for Edge-precise Cloud Detection
-
 - [cloud-buster](https://github.com/azavea/cloud-buster) -> Sentinel-2 L1C and L2A Imagery with Fewer Clouds
 
 - [SatelliteCloudGenerator](https://github.com/cidcom/SatelliteCloudGenerator) -> A PyTorch-based tool to generate clouds for satellite images
@@ -2600,7 +2596,7 @@ Image-to-image translation is a crucial aspect of computer vision that utilizes
 
 - [A growing problem of ‘deepfake geography’: How AI falsifies satellite images](https://www.washington.edu/news/2021/04/21/a-growing-problem-of-deepfake-geography-how-ai-falsifies-satellite-images/)
 
-- [Kaggle Pix2Pix Maps](https://www.kaggle.com/alincijov/pix2pix-maps) -> dataset for pix2pix to take a google map satellite photo and build a street map
+- [Kaggle Pix2Pix Maps](https://www.kaggle.com/datasets/alincijov/pix2pix-maps) -> dataset for pix2pix to take a google map satellite photo and build a street map
 
 - [guided-deep-decoder](https://github.com/tuezato/guided-deep-decoder) -> With guided deep decoder, you can solve different image pair fusion problems, allowing super-resolution, pansharpening or denoising
 
@@ -2950,8 +2946,6 @@ Mixed data learning is the process of learning from datasets that may contain an
 
 - [Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps](https://arxiv.org/abs/1705.06057) -> fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks, arxiv paper
 
-- [Composing Decision Forest and Neural Network models](https://www.tensorflow.org/decision_forests/tutorials/model_composition_colab) tensorflow documentation
-
 - [pyimagesearch article on mixed-data](https://www.pyimagesearch.com/2019/02/04/keras-multiple-inputs-and-mixed-data/)
 
 - [pytorch-widedeep](https://github.com/jrzaurin/pytorch-widedeep) -> A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
@@ -3216,7 +3210,7 @@ Image registration is the process of registering one or more images onto another
 
 - [ImageRegistration](https://github.com/jandremarais/ImageRegistration) -> Interview assignment for multimodal image registration using SIFT
 
-- [imreg_dft](https://github.com/matejak/imreg_dft) -> Image registration using discrete Fourier transform. Given two images it can calculate the difference between scale, rotation and position of imaged features. Used by the [up42 co-registration service](https://up42.com/marketplace/blocks/processing/up42-coregistration)
+- [imreg_dft](https://github.com/matejak/imreg_dft) -> Image registration using discrete Fourier transform. Given two images it can calculate the difference between scale, rotation and position of imaged features.
 
 - [arosics](https://danschef.git-pages.gfz-potsdam.de/arosics/doc/about.html) -> Perform automatic subpixel co-registration of two satellite image datasets using phase-correlation, XY translations only.