Update README.md

Author: robmarkcole

README.md

@@ -84,6 +84,7 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [SSRN](https://github.com/zilongzhong/SSRN) -> Implementation of SSRN for Hyperspectral Image Classification
 * [Implementing Transfer Learning from RGB to Multi-channel Imagery](https://towardsdatascience.com/implementing-transfer-learning-from-rgb-to-multi-channel-imagery-f87924679166) -> Semantic Segmentation using ResNet50 Backbone plus Pyramid Pooling
 * [clusternet_segmentation](https://github.com/zhygallo/clusternet_segmentation) -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network
+* [DCA](https://github.com/Luffy03/DCA) -> Code for TGRS 2022 paper "Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation"
 
 ### Semantic segmentation - multiclass classification
 * [Land Cover Classification with U-Net](https://baratam-tarunkumar.medium.com/land-cover-classification-with-u-net-aa618ea64a1b) -> Satellite Image Multi-Class Semantic Segmentation Task with PyTorch Implementation of U-Net
@@ -134,6 +135,7 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [segmentation-enhanced-resunet](https://github.com/tranleanh/segmentation-enhanced-resunet) -> Urban building extraction in Daejeon region using Modified Residual U-Net (Modified ResUnet) and applying post-processing
 * [Mask RCNN for Spacenet Off Nadir Building Detection](https://github.com/ashnair1/Mask-RCNN-for-Off-Nadir-Building-Detection)
 * [GRSL_BFE_MA](https://github.com/jiankang1991/GRSL_BFE_MA) -> Deep Learning-based Building Footprint Extraction with Missing Annotations using a novel loss function
+* [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, with [paper](https://www.mdpi.com/2072-4292/12/14/2240/htm)
 
 ### Semantic segmentation - solar panels
 * [DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States](https://www.cell.com/joule/fulltext/S2542-4351(18)30570-1) -> with [website](http://web.stanford.edu/group/deepsolar/home), [repo](https://github.com/wangzhecheng/DeepSolar) and [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
@@ -148,6 +150,8 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data](https://medium.com/nam-r/predicting-the-solar-potential-of-rooftops-using-image-segmentation-and-structured-data-61198c39d57c) Medium article, using 20cm imagery & Unet
 * [solar-pv-global-inventory](https://github.com/Lkruitwagen/solar-pv-global-inventory) -> code from the Nature paper of Kruitwagen et al, used to produce a global inventory of utility-scale solar photvoltaic generating stations
 * [remote-sensing-solar-pv](https://github.com/Lkruitwagen/remote-sensing-solar-pv) -> A repository for sharing progress on the automated detection of solar PV arrays in sentinel-2 remote sensing imagery
+* [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
 
 ### Semantic segmentation - roads
 * [Semantic segmentation of roads and highways using Sentinel-2 imagery (10m) super-resolved using the SENX4 model up to x4 the initial spatial resolution (2.5m)](https://tracasa.es/innovative-stories/sen2roadlasviastambiensevendesdesentinel-2/) (results, no repo)
@@ -461,6 +465,7 @@ The goal is to predict economic activity from satellite imagery rather than cond
 * [Deep transfer learning techniques for crop yield prediction, published in COMPASS 2018](https://github.com/AnnaXWang/deep-transfer-learning-crop-prediction)
 * [Understanding crop yield predictions from CNNs](https://github.com/brad-ross/crop-yield-prediction-project)
 * [Advanced Deep Learning Techniques for Predicting Maize Crop Yield using Sentinel-2 Satellite Imagery](https://zionayomide.medium.com/advanced-deep-learning-techniques-for-predicting-maize-crop-yield-using-sentinel-2-satellite-1b63ac8b0789)
+* [pycrop-yield-prediction](https://github.com/gabrieltseng/pycrop-yield-prediction) -> A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction
 
 ## Disaster response
 * [DisaVu](https://github.com/SrzStephen/DisaVu) -> combines building & damage detection and provides an app for viewing predictions
@@ -481,6 +486,7 @@ Super-resolution attempts to enhance the resolution of an imaging system, and ca
 * [AI-based Super resolution and change detection to enforce Sentinel-2 systematic usage](https://medium.com/@sistema_gmbh/ai-based-super-resolution-and-change-detection-to-enforce-sentinel-2-systematic-usage-65aa37d0365) -> Worldview-2 images (2m) were used to create a reference dataset and increase the spatial resolution of the Copernicus sensor from 10m to 5m
 * [SRCDNet](https://github.com/liumency/SRCDNet) -> The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ". SRCDNet is designed to learn and predict change maps from bi-temporal images with different resolutions
 * [Model-Guided Deep Hyperspectral Image Super-resolution](https://github.com/chengerr/Model-Guided-Deep-Hyperspectral-Image-Super-resolution) -> code accompanying the paper [Model-Guided Deep Hyperspectral Image Super-Resolution](https://ieeexplore.ieee.org/document/9429905)
+* [Super-resolving beyond satellite hardware](https://github.com/smpetrie/superres) -> [paper](https://arxiv.org/abs/2103.06270) assessing SR performance in reconstructing realistically degraded satellite images
 
 ### Single image super resolution (SISR)
 * [Super Resolution for Satellite Imagery - srcnn repo](https://github.com/WarrenGreen/srcnn)
@@ -865,6 +871,12 @@ This section contains a short list of datasets relevant to deep learning, partic
 * https://sites.google.com/view/zhouwx/dataset?authuser=0
 * Publication: [PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval](https://arxiv.org/abs/1706.03424)
 
+## Million-AID
+* https://captain-whu.github.io/DiRS/
+* a new large-scale benchmark dataset containing million instances for RS scene classification
+* 51 scene categories organized by the hierarchical category
+* [Pretrained models](https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing)
+
 ## FAIR1M object detection dataset
 * [FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery](https://arxiv.org/abs/2103.05569)
 * Download at [gaofen-challenge.com](http://gaofen-challenge.com/)