Update README.md

Author: robmarkcole

README.md

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 # satellite-image-deep-learning
-This document lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research *and* developer communities. If you find this work useful please give it a star and considering following me on the platforms below, thanks!
+This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research *and* developer communities. If you find this work useful please give it a star and consider sponsoring it. You can also follow me on the platforms below where I aim to post daily updates on my new discoveries. Thanks!
 
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@@ -801,6 +801,7 @@ Generally treated as a semantic segmentation problem or custom features created
 * [AISD](https://github.com/RSrscoder/AISD) -> code (Matlab) and dataset for 2020 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0924271620302045): Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset
 * [CloudGAN](https://github.com/JerrySchonenberg/CloudGAN) -> Detecting and Removing Clouds from RGB-images using Image Inpainting
 * [Using GANs to Augment Data for Cloud Image Segmentation Task](https://github.com/jain15mayank/GAN-augmentation-cloud-image-segmentation) -> code for 2021 [paper](https://arxiv.org/abs/2106.03064)
+* [Cloud-Segmentation-from-Satellite-Imagery](https://github.com/vedantk-b/Cloud-Segmentation-from-Satellite-Imagery) -> applied to Sentinel-2 dataset
 
 ## 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..!
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 * [DeepAerialMatching_pytorch](https://github.com/jaehyunnn/DeepAerialMatching_pytorch) -> code for 2020 [paper](https://arxiv.org/abs/2002.01325): A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching
 * [DPCN](https://github.com/ZJU-Robotics-Lab/DPCN) -> code for 2020 [paper](https://arxiv.org/abs/2008.09474): Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
 * [FSRA](https://github.com/Dmmm1997/FSRA) -> code for 2022 [paper](https://arxiv.org/abs/2201.09206): A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
+* [IHN](https://github.com/imdumpl78/IHN) -> code for 2022 [paper](https://arxiv.org/abs/2203.15982): Iterative Deep Homography Estimation
 
 ## Data fusion
 Data fusion covers techniques which integrate multiple datasources, for example fusing SAR & optical to make predictions about crop type. It can also cover fusion with non imagery data such as IOT sensor data
@@ -2430,6 +2432,7 @@ Since raw images can be very large, it is usually necessary to chip/tile them in
 * [split-rs-data](https://github.com/Youssef-Harby/split-rs-data) -> Divide remote sensing images and their labels into data sets of specified size
 * [image-reconstructor-patches](https://github.com/marijavella/image-reconstructor-patches) -> Reconstruct Image from Patches with a Variable Stride
 * [rpc_cropper](https://github.com/carlodef/rpc_cropper) -> A small standalone tool to crop satellite images and their RPC
+* [geotile](https://github.com/iamtekson/geotile) -> python library for tiling the geographic raster data
 
 ## Image dataset creation
 Many datasets on kaggle & elsewhere have been created by screen-clipping Google Maps or browsing web portals. The tools below are to create datasets programatically
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 * [libpredict](https://github.com/la1k/libpredict) -> satellite orbit prediction library
 * [GOTCHA](https://github.com/jveitchmichaelis/gotcha) -> Command line implementation of the GOTCHA stereo matching algorithm
 * [SREM](https://github.com/oyam/srem) -> A Simplified and Robust Surface Reflectance Estimation Method for Satellite Imagery
+* [kaizen](https://github.com/fuzailpalnak/kaizen) -> A library to map match and help tackle the problem of overlapping/intersecting road and building footprint that arises in the process of map making
 
 ## GDAL & Rasterio
 So improtant this pair gets their own section. GDAL is THE command line tool for reading and writing raster and vector geospatial data formats. If you are using python you will probably want to use Rasterio which provides a pythonic wrapper for GDAL