remove bad url

README.md

@@ -260,7 +260,7 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [SOLC](https://github.com/yisun98/SOLC) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/pii/S0303243421003457): MCANet: 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 Use with mUnet
 * [land-cover](https://github.com/lucashu1/land-cover) -> code for 2021 [paper](https://arxiv.org/abs/2008.10351): Model Generalization in Deep Learning Applications for Land Cover Mapping
-* [generalizablersc](https://github.com/dgominski/generalizablersc) -> code for 2022 [paper](https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/papers/Gominski_Cross-Dataset_Learning_for_Generalizable_Land_Use_Scene_Classification_CVPRW_2022_paper.pdf): Cross-dataset Learning for Generalizable Land Use Scene Classification
+* [generalizablersc](https://github.com/dgominski/generalizablersc) -> code for 2022 paper: Cross-dataset Learning for Generalizable Land Use Scene Classification
 * [Large-scale-Automatic-Identification-of-Urban-Vacant-Land](https://github.com/SkydustZ/Large-scale-Automatic-Identification-of-Urban-Vacant-Land) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0169204622000330): Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images
 * [SSLTransformerRS](https://github.com/HSG-AIML/SSLTransformerRS) -> code for 2022 paper: Self-supervised Vision Transformers for Land-cover Segmentation and
 Classification
@@ -322,7 +322,7 @@ Classification
 * [A-U-Net-for-Flood-Extent-Mapping](https://github.com/jorgemspereira/A-U-Net-for-Flood-Extent-Mapping) -> in keras
 * [floatingobjects](https://github.com/ESA-PhiLab/floatingobjects) -> code for the paper: TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITHLEARNED SPATIAL FEATURES USING SENTINEL 2. Uses U-Net & pytorch
 * [River-Network-Extraction-from-Satellite-Image-using-UNet-and-Tensorflow](https://github.com/Diwas524/River-Network-Extraction-from-Satellite-Image-using-UNet-and-Tensorflow) -> uses Sentinel-2 imagery
-* [SpaceNet8](https://github.com/SpaceNetChallenge/SpaceNet8) -> baseline Unet solution to detect flooded roads and buildings. With [paper](https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/html/Hansch_SpaceNet_8_-_The_Detection_of_Flooded_Roads_and_Buildings_CVPRW_2022_paper.html#:~:text=To%20this%20aim%2C%20SpaceNet%208,15%25%20are%20flooded%2C%20respectively.)
+* [SpaceNet8](https://github.com/SpaceNetChallenge/SpaceNet8) -> baseline Unet solution to detect flooded roads and buildings
 * [dlsim](https://github.com/nyokoya/dlsim) -> code for 2020 [paper](https://arxiv.org/abs/2006.05180): Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
 
 ### Segmentation - Fire, smoke & burn areas
@@ -1099,7 +1099,7 @@ Image fusion of low res multispectral with high res pan band.
 * [AFPN](https://github.com/yisun98/AFPN) -> Adaptive Detail Injection-Based Feature Pyramid Network For Pan-sharpening
 * [pan-sharpening](https://github.com/yisun98/pan-sharpening) -> multiple methods demonstrated for multispectral and panchromatic images
 * [PSGan-Family](https://github.com/zhysora/PSGan-Family) -> code for 2020 [paper](https://ieeexplore.ieee.org/document/9306912): PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening
-* [PanNet-Landsat](https://github.com/oyam/PanNet-Landsat) -> code for 2017 [paper](https://openaccess.thecvf.com/content_iccv_2017/html/Yang_PanNet_A_Deep_ICCV_2017_paper.html): A Deep Network Architecture for Pan-Sharpening
+* [PanNet-Landsat](https://github.com/oyam/PanNet-Landsat) -> code for 2017 paper: A Deep Network Architecture for Pan-Sharpening
 * [DLPan-Toolbox](https://github.com/liangjiandeng/DLPan-Toolbox) -> code for 2022 paper: Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks
 * [LPPN](https://github.com/ChengJin-git/LPPN) -> code for 2021 [paper](https://www.sciencedirect.com/science/article/abs/pii/S1566253521001809): Laplacian pyramid networks: A new approach for multispectral pansharpening
 * [S2_SSC_CNN](https://github.com/hvn2/S2_SSC_CNN) -> code for 2020 [paper](https://ieeexplore.ieee.org/document/9323614): Zero-shot Sentinel-2 Sharpening Using A Symmetric Skipped Connection Convolutional Neural Network
@@ -1217,7 +1217,6 @@ These techniques combine multiple data types, e.g. imagery and text data.
 * [Predicting the locations of traffic accidents with satellite imagery and convolutional neural networks](https://towardsdatascience.com/teaching-a-neural-network-to-see-roads-74bff240c3e5) -> Combining satellite imagery and structured data to predict the location of traffic accidents with a neural network of neural networks, with [repo](https://github.com/L-Lewis/Predicting-traffic-accidents-CNN)
 * [Multi-Input Deep Neural Networks with PyTorch-Lightning - Combine Image and Tabular Data](https://rosenfelder.ai/multi-input-neural-network-pytorch/) -> excellent intro article using pytorch, not actually applied to satellite data but to real estate data, with [repo](https://github.com/MarkusRosen/pytorch_multi_input_example)
 * [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
-* [Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories](https://openaccess.thecvf.com/content/ICCV2021/html/He_Inferring_High-Resolution_Traffic_Accident_Risk_Maps_Based_on_Satellite_Imagery_ICCV_2021_paper.html) -> input satellite imagery, GPS trajectories, road maps and the history of accidents to generate high-resolution (5 meters) accident risk maps
 * [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
@@ -1393,7 +1392,6 @@ Measure surface contours & locate 3D points in space from 2D images. NeRF stands
 * [ArcGIS can generate DEMs from stereo images](http://pro.arcgis.com/en/pro-app/help/data/imagery/generate-elevation-data-using-the-dems-wizard.htm)
 * https://github.com/MISS3D/s2p -> produces elevation models from images taken by high resolution optical satellites -> demo code on https://gfacciol.github.io/IS18/
 * [Automatic 3D Reconstruction from Multi-Date Satellite Images](http://dev.ipol.im/~facciolo/pub/CVPRW2017.pdf)
-* [Semi-global matching with neural networks](http://openaccess.thecvf.com/content_cvpr_2017/papers/Seki_SGM-Nets_Semi-Global_Matching_CVPR_2017_paper.pdf)
 * [Predict the fate of glaciers](https://github.com/geohackweek/glacierhack_2018)
 * [monodepth - Unsupervised single image depth prediction with CNNs](https://github.com/mrharicot/monodepth)
 * [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://github.com/jzbontar/mc-cnn)
@@ -1450,7 +1448,7 @@ Measure surface contours & locate 3D points in space from 2D images. NeRF stands
 * [Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training](https://github.com/sidgan/ETCI-2021-Competition-on-Flood-Detection)
 * [SpaceNet_SAR_Buildings_Solutions](https://github.com/SpaceNetChallenge/SpaceNet_SAR_Buildings_Solutions) -> The winning solutions for the SpaceNet 6 Challenge
 * [Mapping and monitoring of infrastructure in desert regions with Sentinel-1](https://github.com/ESA-PhiLab/infrastructure)
-* [xView3](https://iuu.xview.us/) is a competition to detect dark vessels using computer vision and global SAR satellite imagery. [First place solution](https://github.com/DIUx-xView/xView3_first_place) and [second place solution](https://github.com/DIUx-xView/xView3_second_place). Additional places up to fifth place are available at the (xView GitHub Organization page)[https://github.com/DIUx-xView/].
+* [xView3](https://iuu.xview.us/) is a competition to detect dark vessels using computer vision and global SAR satellite imagery. [First place solution](https://github.com/DIUx-xView/xView3_first_place) and [second place solution](https://github.com/DIUx-xView/xView3_second_place). Additional places up to fifth place are available at the [xView GitHub Organization page](https://github.com/DIUx-xView/)
 * [Winners of the STAC Overflow: Map Floodwater from Radar Imagery competition](https://github.com/drivendataorg/stac-overflow)
 * [deSpeckNet-TF-GEE](https://github.com/adugnag/deSpeckNet-TF-GEE) -> implementation of the paper 'deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling'
 * [cnn_sar_image_classification](https://github.com/diogosens/cnn_sar_image_classification) -> CNN for classifying SAR images of the Amazon Rainforest
@@ -2231,7 +2229,7 @@ An overview of the most relevant services provided by AWS, Google and Microsoft.
 * [Using artificial intelligence to detect product defects with AWS Step Functions](https://aws.amazon.com/blogs/compute/using-artificial-intelligence-to-detect-product-defects-with-aws-step-functions/) -> demonstrates image classification workflow
 * [sagemaker-defect-detection](https://github.com/awslabs/sagemaker-defect-detection) -> demonstrates object detection training and deployment
 * [How do you process space data and imagery in low earth orbit?](https://www.aboutamazon.com/news/aws/how-do-you-process-space-data-and-imagery-in-low-earth-orbit) -> Snowcone is a standalone computer that can run AWS services at the edge, and has been demonstraed on the ISS (International space station)
-* [Amazon OpenSearch](https://aws.amazon.com/opensearch-service/) -> can be used [to create a visual search service](https://github.com/aws-samples/amazon-sagemaker-visual-search)
+* [Amazon OpenSearch](https://aws.amazon.com/opensearch-service/) -> can be used to create a visual search service
 * [Automated Earth observation using AWS Ground Station Amazon S3 data delivery](https://aws.amazon.com/blogs/publicsector/automated-earth-observation-aws-ground-station-amazon-s3-data-delivery/)
 * [Satellogic makes Earth observation data more accessible and affordable with AWS](https://aws.amazon.com/blogs/publicsector/satellogic-makes-earth-observation-data-more-accessible-affordable-aws/)
 * [Analyze terabyte-scale geospatial datasets with Dask and Jupyter on AWS](https://aws.amazon.com/blogs/publicsector/analyze-terabyte-scale-geospatial-datasets-with-dask-and-jupyter-on-aws/)