Update README.md

Author: robmarkcole

README.md

@@ -97,6 +97,7 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [Satellite-Segmentation](https://github.com/AstarLight/Satellite-Segmentation) -> model emsamble: SegNet + U-Net in keras
 * [IEEE_TGRS_SpectralFormer](https://github.com/danfenghong/IEEE_TGRS_SpectralFormer) -> code for 2021 paper: Spectralformer: Rethinking hyperspectral image classification with transformers
 * [Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels](https://github.com/mpBarbato/Unsupervised-Segmentation-of-Hyperspectral-Remote-Sensing-Images-with-Superpixels)
+* [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?via%3Dihub): Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images
 
 ### 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, uses DeepGlobe Land Cover Segmentation dataset, with [code](https://github.com/TarunKumar1995-glitch/land_cover_classification_unet)
@@ -233,6 +234,7 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 * [IndustrialSmokePlumeDetection](https://github.com/HSG-AIML/IndustrialSmokePlumeDetection) -> using Sentinel-2 & a modified ResNet-50
 * [burned-area-detection](https://github.com/dymaxionlabs/burned-area-detection) -> uses Sentinel-2
 * [rescue](https://github.com/dbdmg/rescue) -> code of the paper: Attention to fires: multi-channel deep-learning models forwildfire severity prediction
+* [smoke_segmentation](https://github.com/jeffwen/smoke_segmentation) -> Segmenting smoke plumes and predicting density from GOES imagery
 
 ### Semantic segmentation - glaciers
 * [HED-UNet](https://github.com/khdlr/HED-UNet) -> a model for simultaneous semantic segmentation and edge detection, examples provided are glacier fronts and building footprints using the Inria Aerial Image Labeling dataset
@@ -332,11 +334,6 @@ Several different techniques can be used to count the number of objects in an im
 * [Google-earth-Object-Recognition](https://github.com/InnovAIco/Google-earth-Object-Recognition) -> Code for training and evaluating on Dior Dataset (Google Earth Images) using RetinaNet and YOLOV5
 * [AI-TOD](https://github.com/jwwangchn/AI-TOD) -> a dataset for tiny object detection in aerial images. The mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than other datasets
 
-#### Object detection enhanced by super resolution
-* [Super-Resolution and Object Detection](https://medium.com/the-downlinq/super-resolution-and-object-detection-a-love-story-part-4-8ad971eef81e) -> Super-resolution is a relatively inexpensive enhancement that can improve object detection performance
-* [EESRGAN](https://github.com/Jakaria08/EESRGAN) -> Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
-* [Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation](https://www.preprints.org/manuscript/202108.0337/v1)
-
 #### Object detection with rotated bounding boxes
 * OBB: orinted bounding boxes are polygons representing rotated rectangles
 * For datasets checkout [DOTA](https://github.com/robmarkcole/satellite-image-deep-learning#dota-object-detection-dataset) & [HRSC2016](https://github.com/robmarkcole/satellite-image-deep-learning#hrsc2016-ship-object-detection-dataset)
@@ -361,6 +358,12 @@ Several different techniques can be used to count the number of objects in an im
 * [Seodore](https://github.com/nijkah/Seodore) -> framework maintaining recent updates of mmdetection
 * [Rotation-RetinaNet-PyTorch](https://github.com/HsLOL/Rotation-RetinaNet-PyTorch) -> oriented detector Rotation-RetinaNet implementation on Optical and SAR ship dataset
 * [AIDet](https://github.com/jwwangchn/aidet) -> an open source object detection in aerial image toolbox based on MMDetection
+* [rotation-yolov5](https://github.com/BossZard/rotation-yolov5) -> rotation detection based on yolov5
+
+#### Object detection enhanced by super resolution
+* [Super-Resolution and Object Detection](https://medium.com/the-downlinq/super-resolution-and-object-detection-a-love-story-part-4-8ad971eef81e) -> Super-resolution is a relatively inexpensive enhancement that can improve object detection performance
+* [EESRGAN](https://github.com/Jakaria08/EESRGAN) -> Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
+* [Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation](https://www.preprints.org/manuscript/202108.0337/v1)
 
 #### Object detection - buildings, rooftops & solar panels
 * [Machine Learning For Rooftop Detection and Solar Panel Installment](https://omdena.com/blog/machine-learning-rooftops/) discusses tiling large images and generating annotations from OSM data. Features of the roofs were calculated using a combination of contour detection and classification. [Follow up article using semantic segmentation](https://omdena.com/blog/rooftops-classification/)
@@ -479,6 +482,7 @@ Generally treated as a semantic segmentation problem or custom features created
 * [CloudX-Net](https://github.com/sumitkanu/CloudX-Net) -> an efficient and robust architecture used for detection of clouds from satellite images
 * [A simple cloud-detection walk-through using Convolutional Neural Network (CNN and U-Net) and fast.ai library](https://medium.com/analytics-vidhya/a-simple-cloud-detection-walk-through-using-convolutional-neural-network-cnn-and-u-net-and-bc745dda4b04)
 * [38Cloud-Medium](https://github.com/cordmaur/38Cloud-Medium) -> Walk-through using u-net to detect clouds in satellite images with fast.ai
+* [cloud_detection_using_satellite_data](https://github.com/ZhouPeng-NIMST/cloud_detection_using_satellite_data) -> performed on Sentinel 2 data
 
 ## Change detection & time-series
 Monitor water levels, coast lines, size of urban areas, wildfire damage, crop changes. Note, clouds change often too..!
@@ -546,6 +550,7 @@ Monitor water levels, coast lines, size of urban areas, wildfire damage, crop ch
 * [timematch](https://github.com/jnyborg/timematch) -> code for 2022 paper: A method to perform unsupervised cross-region adaptation of crop classifiers trained with satellite image time series. We also introduce an open-access dataset for cross-region adaptation with SITS from four different regions in Europe
 * [LGPNet-BCD](https://github.com/TongfeiLiu/LGPNet-BCD) -> code for 2021 paper: Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy
 * [DS_UNet](https://github.com/SebastianHafner/DS_UNet) -> code for 2021 paper: Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset
+* [SiameseSSL](https://github.com/SebastianHafner/SiameseSSL) -> code for 2022 [paper](https://arxiv.org/abs/2204.12202): Urban change detection with a Dual-Task Siamese network and semi-supervised learning. Uses SpaceNet 7 dataset
 
 ## Wealth and economic activity
 The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys
@@ -832,6 +837,7 @@ Image registration is the process of transforming different sets of data into on
 * [cnn-matching](https://github.com/lan-cz/cnn-matching) -> code and datadset for paper: Deep learning algorithm for feature matching of cross modality remote sensing images
 * [DS_UNet](https://github.com/SebastianHafner/DS_UNet) -> code for 2021 paper: Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset
 * [DDA_UrbanExtraction](https://github.com/SebastianHafner/DDA_UrbanExtraction) -> Unsupervised Domain Adaptation for Global Urban Extraction using Sentinel-1 and Sentinel-2 Data
+* [Imatch-P](https://github.com/geoyee/Imatch-P) -> A demo using SuperGlue and SuperPoint to do the image matching task based PaddlePaddle
 
 ## Terrain mapping, Lidar & DEMs
 Measure surface contours.
@@ -865,6 +871,8 @@ Measure surface contours.
 * [aerial_mtl](https://github.com/marcelampc/aerial_mtl) -> PyTorch implementation for multi-task learning with aerial images to learn both semantics and height from aerial image datasets; fuses RGB & lidar
 * [ReKlaSat-3D](https://github.com/MacOS/ReKlaSat-3D) -> 3D Reconstruction and Classification from Very High Resolution Satellite Imagery
 * [M3Net](https://github.com/lauraset/BuildingHeightModel) -> A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas
+* [HMSM-Net](https://github.com/Sheng029/HMSM-Net) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/abs/pii/S092427162200123X): Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images
+* [StereoMatchingRemoteSensing](https://github.com/Sheng029/StereoMatchingRemoteSensing) -> code for 2021 [paper](https://www.mdpi.com/2072-4292/13/24/5050): Dual-Scale Matching Network for Disparity Estimation of High-Resolution Remote Sensing Images
 
 ## Thermal Infrared
 * [The World Needs (a lot) More Thermal Infrared Data from Space](https://towardsdatascience.com/the-world-needs-a-lot-more-thermal-infrared-data-from-space-dbbba389be8a)
@@ -1729,6 +1737,7 @@ Scripts and command line applications.
 * [arop](https://github.com/george-silva/arop) -> Automated Registration and Orthorectification Package
 * [satellite_image](https://github.com/dgketchum/satellite_image) -> Python package to process images from Landsat satellites and return geographic information, cloud mask, numpy array, geotiff
 * [large_image](https://github.com/girder/large_image) -> Python modules to work with large multiresolution images
+* [ResizeRight](https://github.com/assafshocher/ResizeRight) -> The correct way to resize images or tensors. For Numpy or Pytorch (differentiable)
 
 ## Image chipping/tiling & merging
 Since raw images can be very large, it is usually necessary to chip/tile them into smaller images before annotation & training
@@ -1848,6 +1857,7 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [H3-Pandas](https://github.com/DahnJ/H3-Pandas) -> Integrates H3 with GeoPandas and Pandas
 * [gmplot](https://github.com/gmplot/gmplot) -> a matplotlib-like interface to render all the data you'd like on top of Google Maps
 * [NPYViewer](https://github.com/csmailis/NPYViewer) ->  a simple GUI tool that provides multiple ways to view `.npy` files containing 2D NumPy Arrays
+* [pyGEOVis](https://github.com/geoyee/pyGEOVis) -> Visualize geo-tiff/json based on folium
 
 ## Algorithms
 * [WaterDetect](https://github.com/cordmaur/WaterDetect) -> an end-to-end algorithm to generate open water cover mask, specially conceived for L2A Sentinel 2 imagery. It can also be used for Landsat 8 images and for other multispectral clustering/segmentation tasks.