Update README.md

Author: robmarkcole

README.md

@@ -270,6 +270,9 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [ASLNet](https://github.com/ggsDing/ASLNet) -> code for 2021 [paper](https://ieeexplore.ieee.org/document/9653801): Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
 * [BRRNet](https://github.com/wangyi111/Building-Extraction) -> implementation of Modified U-Net from 2020 [paper](https://www.mdpi.com/2072-4292/12/6/1050): BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images
 * [Multi-Scale-Filtering-Building-Index](https://github.com/ThomasWangWeiHong/Multi-Scale-Filtering-Building-Index) -> Python implementation of building extraction index proposed in 2019 [paper](https://www.mdpi.com/2072-4292/11/5/482): A Multi - Scale Filtering Building Index for Building Extraction in Very High - Resolution Satellite Imagery
+* [Models for Remote Sensing](https://github.com/bohaohuang/mrs) -> long list of unets etc applied to building detection
+* [boundary_loss_for_remote_sensing](https://github.com/yiskw713/boundary_loss_for_remote_sensing) -> code for 2019 paper: Boundary Loss for Remote Sensing Imagery
+Semantic Segmentation
 
 ### Segmentation - Roads
 Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment
@@ -299,6 +302,7 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 * [FCNs-for-road-extraction-keras](https://github.com/zetrun-liu/FCNs-for-road-extraction-keras) -> Road extraction of high-resolution remote sensing images based on various semantic segmentation networks
 * [cresi](https://github.com/avanetten/cresi) -> Road network extraction from satellite imagery, with speed and travel time estimates
 * [road-extraction-d-linknet](https://github.com/NekoApocalypse/road-extraction-d-linknet) -> code for 2018 [paper](https://ieeexplore.ieee.org/document/8575492): D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
+* [Sat2Graph](https://github.com/songtaohe/Sat2Graph) -> code for 2020 paper: Road Graph Extraction through Graph-Tensor Encoding
 
 ### Segmentation - Vegetation, crops & crop boundaries
 * [Сrор field boundary detection: approaches overview and main challenges](https://soilmate.medium.com/%D1%81r%D0%BE%D1%80-field-boundary-detection-approaches-overview-and-main-challenges-53736725cb06) - review article, no code
@@ -391,6 +395,7 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 * [solar_seg](https://github.com/tcapelle/solar_seg) -> Solar segmentation of PV modules (sub elements of panels) using drone images and fast.ai
 * [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
+* [adopptrs](https://github.com/francois-rozet/adopptrs) -> Automatic Detection Of Photovoltaic Panels Through Remote Sensing using unet & pytorch
 
 ### Segmentation - electrical substations
 The repos below resulted from the [ICETCI 2021 competition on Machine Learning based feature extraction of Electrical Substations from Satellite Data using Open Source Tools](https://competitions.codalab.org/competitions/32132)
@@ -583,6 +588,7 @@ Detecting the most noticeable or important object in a scene
 * [Deep-Learning-for-Aircraft-Recognition](https://github.com/Shayan-Bravo/Deep-Learning-for-Aircraft-Recognition) -> A CNN model trained to classify and identify various military aircraft through satellite imagery
 * [FRCNN-for-Aircraft-Detection](https://github.com/Huatsing-Lau/FRCNN-for-Aircraft-Detection) -> faster-rcnn & keras
 * [ergo-planes-detector](https://github.com/evilsocket/ergo-planes-detector) -> An ergo based project that relies on a convolutional neural network to detect airplanes from satellite imagery, uses the PlanesNet dataset
+* [pytorch-remote-sensing](https://github.com/miko7879/pytorch-remote-sensing) -> Aircraft detection using the 'Airbus Aircraft Detection' dataset and Faster-RCNN with ResNet-50 backbone using pytorch
 
 #### Object detection - infrastructure & utilities
 * [wind-turbine-detector](https://github.com/lbborkowski/wind-turbine-detector) -> Wind Turbine Object Detection from Aerial Imagery Using TensorFlow Object Detection API
@@ -619,6 +625,7 @@ Oil is stored in tanks at many points between extraction and sale, and the volum
 * [ognet](https://stanfordmlgroup.github.io/projects/ognet/) -> a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
 * [RSOD-Dataset](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-) -> dataset for object detection in PASCAL VOC format. Aircraft, playgrounds, overpasses & oiltanks
 * [oil_storage-detector](https://github.com/TheodorEmanuelsson/oil_storage-detector) -> using yolov5 and the Airbus Oil Storage Detection dataset
+* [oil_well_detector](https://github.com/dzubke/oil_well_detector) -> detect oil wells in the Bakken oil field based on satellite imagery
 
 ## Cloud detection & removal
 Generally treated as a semantic segmentation problem or custom features created using band math
@@ -663,6 +670,8 @@ Generally treated as a semantic segmentation problem or custom features created
 * [CNN-based-Cloud-Detection-Methods](https://github.com/LK-Peng/CNN-based-Cloud-Detection-Methods) -> Understanding the Role of Receptive Field of Convolutional Neural Network for Cloud Detection in Landsat 8 OLI Imagery
 * [cloud-removal-deploy](https://github.com/XavierJiezou/cloud-removal-deploy) -> flask app for cloud removal
 * [CloudMattingGAN](https://github.com/flyakon/CloudMattingGAN) -> code for 2019 [paper](https://ieeexplore.ieee.org/document/9009465): Generative Adversarial Training for Weakly Supervised Cloud Matting
+* [atrain-cloudseg](https://github.com/seanremy/atrain-cloudseg) -> Official repository for the A-Train Cloud Segmentation Dataset
+* [CDnet](https://github.com/nkszjx/CDnet-pytorch-master) -> code for 2019 paper: CNN-Based Cloud Detection for Remote Sensing Imager
 
 ## Change detection
 Generally speaking, change detection methods are applied to a pair of images to generate a mask of change, e.g. of buildings damaged in a disaster. Note, clouds & shadows change often too..!
@@ -734,6 +743,7 @@ Generally speaking, change detection methods are applied to a pair of images to
 * [hyperdimensionalCD](https://github.com/sudipansaha/hyperdimensionalCD) -> code for 2021 [paper](https://ieeexplore.ieee.org/abstract/document/9582825): Change Detection in Hyperdimensional Images Using Untrained Models
 * [DSFANet](https://github.com/wwdAlger/DSFANet) -> code for 2018 [paper](https://arxiv.org/abs/1812.00645): Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
 * [FCD-GAN-pytorch](https://github.com/Cwuwhu/FCD-GAN-pytorch) -> Fully Convolutional Change Detection Framework with Generative Adversarial Network (FCD-GAN) is a framework for change detection in multi-temporal remote sensing images
+* [DARNet-CD](https://github.com/jimmyli08/DARNet-CD) -> code for 2022 paper: A Densely Attentive Refinement Network for Change Detection Based on Very-High-Resolution Bitemporal Remote Sensing Images
 
 ## Time-series
 More general than change detection, time series observations can be used for applications including improving the accuracy of crop classification, or predicting future patterns & events. Crop yield is very typically application and has its own section below
@@ -862,6 +872,8 @@ Super-resolution attempts to enhance the resolution of an imaging system, and ca
 * [Optical-RemoteSensing-Image-Resolution](https://github.com/wenjiaXu/Optical-RemoteSensing-Image-Resolution) -> code for 2018 [paper](https://www.mdpi.com/2072-4292/10/12/1893): Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration. Two applications: Gaussian image denoising and single image super-resolution
 * [HSENet](https://github.com/Shaosifan/HSENet) -> code for 2021 [paper](https://ieeexplore.ieee.org/document/9400474): Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution
 * [SR_RemoteSensing](https://github.com/Jing25/SR_RemoteSensing) -> Super-Resolution deep learning models for remote sensing data based on [BasicSR](https://github.com/XPixelGroup/BasicSR)
+* [RSI-Net](https://github.com/EricBrock/RSI-Net) -> code for 2022 paper: A Deep Multi-task Convolutional Neural Network for Remote Sensing Image Super-resolution and Colorization
+* [EDSR-Super-Resolution](https://github.com/RakeshRaj97/EDSR-Super-Resolution) -> EDSR model using PyTorch applied to satellite imagery
 
 ### Multi image super-resolution (MISR)
 Note that nearly all the MISR publications resulted from the [PROBA-V Super Resolution competition](https://kelvins.esa.int/proba-v-super-resolution/)
@@ -1072,6 +1084,7 @@ These techniques combine multiple data types, e.g. imagery and text data.
 * [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
+* [accidentRiskMap](https://github.com/songtaohe/accidentRiskMap) -> Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories
 
 ## Image registration & data fusion
 Image registration & data fusion are the process of transforming different sets of data into one coordinate system. Image registration is the traditional term for this technique as applied to pairs of images, whilst data fusion is a more contemporary and general term which can apply to fusing imagery with non imagery data, such as IOT sensor data
@@ -1122,6 +1135,9 @@ Image registration & data fusion are the process of transforming different sets
 * [Awesome-Data-Fusion-for-Remote-Sensing](https://github.com/px39n/Awesome-Data-Fusion-for-Remote-Sensing)
 * [semantic-template-matching](https://github.com/liliangzhi110/semantictemplatematching) -> code for 2021 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0924271621002446): A deep learning semantic template matching framework for remote sensing image registration
 * [cross-view-image-matching](https://github.com/kregmi/cross-view-image-matching) -> code for 2019 paper: Bridging the Domain Gap for Ground-to-Aerial Image Matching
+* [CoF-MSMG-PCNN](https://github.com/WeiTan1992/CoF-MSMG-PCNN) -> code for 2020 paper: Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain
+* [GMN-Generative-Matching-Network](https://github.com/ei1994/GMN-Generative-Matching-Network) -> code for 2018 paper: Deep Generative Matching Network for Optical and SAR Image Registration
+* [robust_matching_network_on_remote_sensing_imagery_pytorch](https://github.com/mrk1992/robust_matching_network_on_remote_sensing_imagery_pytorch) -> code for 2019 paper: A Robust Matching Network for Gradually Estimating Geometric Transformation on Remote Sensing Imagery
 
 ## Terrain mapping, Disparity Estimation, Lidar, DEMs & NeRF
 Measure surface contours & locate 3D points in space from 2D images. NeRF stands for Neural Radiance Fields and is the term used in deep learning communities to describe a model that generates views of complex 3D scenes based on a partial set of 2D images
@@ -1488,6 +1504,7 @@ The [kaggle blog](http://blog.kaggle.com) is an interesting read.
 * [Entry by lopuhin](https://github.com/lopuhin/kaggle-dstl) using UNet with batch-normalization
 * [Multi-class semantic segmentation of satellite images using U-Net](https://github.com/rogerxujiang/dstl_unet) using DSTL dataset, tensorflow 1 & python 2.7. Accompanying [article](https://towardsdatascience.com/dstl-satellite-imagery-contest-on-kaggle-2f3ef7b8ac40)
 * [Deep-Satellite-Image-Segmentation](https://github.com/antoine-spahr/Deep-Satellite-Image-Segmentation)
+* [Dstl-Satellite-Imagery-Feature-Detection-Improved](https://github.com/dsp6414/Dstl-Satellite-Imagery-Feature-Detection-Improved)
 
 ### Kaggle - DeepSat land cover classification
 * https://www.kaggle.com/datasets/crawford/deepsat-sat4 & https://www.kaggle.com/datasets/crawford/deepsat-sat6
@@ -1579,6 +1596,7 @@ In this challenge, you will build a model to classify cloud organization pattern
 * https://www.kaggle.com/airbusgeo/airbus-aircrafts-sample-dataset
 * One hundred civilian airports and over 3000 annotated commercial aircrafts
 * [detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5](https://medium.com/artificialis/detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5-5f3d464b75ad)
+* [pytorch-remote-sensing](https://github.com/miko7879/pytorch-remote-sensing) -> Aircraft detection using the 'Airbus Aircraft Detection' dataset and Faster-RCNN with ResNet-50 backbone in pytorch
 
 ### Kaggle - Airbus oil storage detection dataset
 * https://www.kaggle.com/airbusgeo/airbus-oil-storage-detection-dataset
@@ -1854,6 +1872,7 @@ A common approach to serving up deep learning model inference code is to wrap it
 * [BMW-YOLOv4-Training-Automation](https://github.com/BMW-InnovationLab/BMW-YOLOv4-Training-Automation) -> project that demos training ML model via rest API
 * [Basic REST API for a keras model using FastAPI](https://github.com/SoySauceNZ/backend)
 * [NI4OS-RSSC](https://github.com/risojevicv/NI4OS-RSSC) -> Web Service for Remote Sensing Scene Classification (RS2C) using TensorFlow Serving and Flask
+* [Sat2Graph Inference Server](https://github.com/songtaohe/Sat2Graph/tree/master/docker) -> API in Go for road segmentation model inferencing
 
 ## Framework specific model serving
 If you are happy to live with some lock-in, these are good options: