remove dead and pdf

Author: robmarkcole

README.md

@@ -1424,8 +1424,6 @@ Oil is stored in tanks at many points between extraction and sale, and the volum
 
 - [SubpixelCircleDetection](https://github.com/anttad/SubpixelCircleDetection) -> CIRCULAR-SHAPED OBJECT DETECTION IN LOW RESOLUTION SATELLITE IMAGES
 
-- [oil_storage-detector](https://github.com/TheodorEmanuelsson/oil_storage-detector) -> using yolov5 and the Airbus Oil Storage Detection dataset
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 - [oil_well_detector](https://github.com/dzubke/oil_well_detector) -> detect oil wells in the Bakken oil field based on satellite imagery
 
 
@@ -1630,10 +1628,6 @@ Clouds are a major issue in remote sensing images as they can obscure the underl
 
 - [CloudSEN12](https://github.com/cloudsen12) -> Sentinel 2 cloud dataset with a [varierty of models here](https://github.com/cloudsen12/models)
 
-- From [this article on sentinelhub](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13) there are three popular classical algorithms that detects thresholds in multiple bands in order to identify clouds. In the same article they propose using semantic segmentation combined with a CNN for a cloud classifier (excellent review paper [here](https://arxiv.org/pdf/1704.06857.pdf)), but state that this requires too much compute resources.
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-- [This article](https://www.mdpi.com/2072-4292/8/8/666) compares a number of ML algorithms, random forests, stochastic gradient descent, support vector machines, Bayesian method.
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 - [Segmentation of Clouds in Satellite Images Using Deep Learning](https://medium.com/swlh/segmentation-of-clouds-in-satellite-images-using-deep-learning-a9f56e0aa83d) -> semantic segmentation using a Unet on the Kaggle 38-Cloud dataset
 
 - [Cloud Detection in Satellite Imagery](https://www.azavea.com/blog/2021/02/08/cloud-detection-in-satellite-imagery/) compares FPN+ResNet18 and CheapLab architectures on Sentinel-2 L1C and L2A imagery
@@ -2047,7 +2041,7 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [CMCDNet](https://github.com/CAU-HE/CMCDNet) -> CMCDNet: Cross-modal change detection flood extraction based on convolutional neural network
 
-- [Dsfer-Net](https://github.com/ShizhenChang/Dsfer-Net) -> A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Network [paper](https://arxiv.org/pdf/2304.01101)
+- [Dsfer-Net](https://github.com/ShizhenChang/Dsfer-Net) -> A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Network
 
 - [Simple-Remote-Sensing-Change-Detection-Framework](https://github.com/walking-shadow/Simple-Remote-Sensing-Change-Detection-Framework) -> Simplified implementation of remote sensing change detection based on Pytorch
 
@@ -2136,7 +2130,7 @@ The analysis of time series observations in remote sensing data has numerous app
 
 - [graph-dynamic-earth-net](https://github.com/corentin-dfg/graph-dynamic-earth-net) -> Graph Dynamic Earth Net: Spatio-Temporal Graph Benchmark for Satellite Image Time Series [paper](https://ieeexplore.ieee.org/abstract/document/10281458)
 
-- [multi-stage-convSTAR-network](https://github.com/0zgur0/multi-stage-convSTAR-network) -> Pytorch implementation for hierarchical time series classification with multi-stage convolutional RNN [paper](https://arxiv.org/pdf/2102.08820.pdf)
+- [multi-stage-convSTAR-network](https://github.com/0zgur0/multi-stage-convSTAR-network) -> Pytorch implementation for hierarchical time series classification with multi-stage convolutional RNN
 
 - [RESTORE-DiT](https://github.com/SQD1/RESTORE-DiT) -> Reliable satellite image time series reconstruction by multimodal sequential diffusion transformer
 
@@ -2366,8 +2360,6 @@ Remote sensing images are used in disaster response to identify and assess damag
 
 - [AI Helps Detect Disaster Damage From Satellite Imagery](https://developer.nvidia.com/blog/ai-helps-detect-disaster-damage-from-satellite-imagery/) -> NVIDIA blog post
 
-- [Turkey-Earthquake-2023-Building-Change-Detection](https://github.com/blackshark-ai/Turkey-Earthquake-2023-Building-Change-Detection) -> The repository contains building footprints derived from Maxar open data imagery and change detection results by blackshark-ai
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 - [MS4D-Net-Building-Damage-Assessment](https://github.com/YJ-He/MS4D-Net-Building-Damage-Assessment) -> MS4D-Net: Multitask-Based Semi-Supervised Semantic Segmentation Framework with Perturbed Dual Mean Teachers for Building Damage Assessment from High-Resolution Remote Sensing Imagery
 
 - [DAHiTra](https://github.com/nka77/DAHiTra) -> Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images. Uses xView2 xBD dataset
@@ -2838,8 +2830,6 @@ Generative networks (e.g. GANs and diffusion models) aim to generate new, synthe
 
 - [opt2sar-cyclegan](https://github.com/zzh811/opt2sar-cyclegan) -> Research on SAR image generation method based on non-homologous data
 
-- [sentinel-cgan](https://github.com/softwaremill/sentinel-cgan) -> code for [article](https://blog.softwaremill.com/generative-adversarial-networks-in-satellite-image-datasets-augmentation-b7045d2f51ab): Generative adversarial networks in satellite image datasets augmentation
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 - [Shoreline_Extraction_GAN](https://github.com/mlundine/Shoreline_Extraction_GAN) -> Shoreline extraction via generative adversarial networks, prediction via LSTMs
 
 - [Landsat8-Sentinel2-Fusion](https://github.com/Rohit18/Landsat8-Sentinel2-Fusion) -> Translating Landsat 8 to Sentinel-2 using a GAN
@@ -3465,12 +3455,6 @@ Measure surface contours & locate 3D points in space from 2D images. NeRF stands
 
 - [Intro to depth from stereo](https://github.com/IntelRealSense/librealsense/blob/master/doc/depth-from-stereo.md)
 
-- Map terrain from stereo images to produce a digital elevation model (DEM) -> high resolution & paired images required, typically 0.3 m, e.g. [Worldview](https://dg-cms-uploads-production.s3.amazonaws.com/uploads/document/file/37/DG-WV2ELEVACCRCY-WP.pdf)
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-- Process of creating a DEM [here](https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/327/2016/isprs-archives-XLI-B1-327-2016.pdf)
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-- [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)
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 - [S2P](https://github.com/centreborelli/s2p) -> S2P is a Python library and command line tool that implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos.
 
 - [Predict the fate of glaciers](https://github.com/geohackweek/glacierhack_2018)
@@ -3787,8 +3771,6 @@ Explainable AI (XAI) is a field of artificial intelligence that focuses on devel
 
 - [H2RSVLM](https://github.com/opendatalab/H2RSVLM) -> Towards Helpful and Honest Remote Sensing Large Vision Language Model
 
-- [LLMs & FMs in Smart Agriculture](https://arxiv.org/pdf/2308.06668) -> Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges
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 - [LHRS-Bot](https://github.com/NJU-LHRS/LHRS-Bot) -> Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model
 
 - [Awesome-VLGFM](https://github.com/zytx121/Awesome-VLGFM) -> Towards Vision-Language Geo-Foundation Models: A Survey