Update README.md

Author: robmarkcole

README.md

@@ -16,10 +16,8 @@ This document lists resources for performing deep learning on satellite imagery.
 * [Deploying models](https://github.com/robmarkcole/satellite-image-deep-learning#deploying-models)
 * [Image annotation](https://github.com/robmarkcole/satellite-image-deep-learning#image-annotation)
 * [Open source software](https://github.com/robmarkcole/satellite-image-deep-learning#open-source-software)
+* [Image dataset creation](https://github.com/robmarkcole/satellite-image-deep-learning#image-dataset-creation)
 * [Deep learning packages, frameworks & projects](https://github.com/robmarkcole/satellite-image-deep-learning#deep-learning-packages-frameworks--projects)
-* [Movers and shakers on Github](https://github.com/robmarkcole/satellite-image-deep-learning#movers-and-shakers-on-github)
-* [Companies & organisations on Github](https://github.com/robmarkcole/satellite-image-deep-learning#companies--organisations-on-github)
-* [About the author](https://github.com/robmarkcole/satellite-image-deep-learning#about-the-author)
 
 # Techniques
 This section explores the different deep and machine learning (ML) techniques applied to common problems in satellite imagery analysis. Good background reading is [Deep learning in remote sensing applications: A meta-analysis and review](https://www.iges.or.jp/en/publication_documents/pub/peer/en/6898/Ma+et+al+2019.pdf)
@@ -97,6 +95,7 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [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): Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images
+* [Semantic-Segmentation-with-Sparse-Labels](https://github.com/Hua-YS/Semantic-Segmentation-with-Sparse-Labels) -> codes and data for learning from sparse annotations
 
 ### 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)
@@ -1943,7 +1942,7 @@ A popular open source alternative to ArcGIS, desktop appication written in pytho
 
 ## Parallel procesing with Dask
 Dask provides advanced parallelism and distributed out-of-core computation with a `dask.dataframe` module designed to scale pandas.
-* [Dask](https://docs.dask.org/en/latest/) works with your favorite PyData libraries to provide performance at scale for the tools you love -> checkout [Read and manipulate tiled GeoTIFF datasets](https://examples.dask.org/applications/satellite-imagery-geotiff.html#)
+* [Dask](https://docs.dask.org/en/latest/) works with your favorite PyData libraries to provide performance at scale for the tools you love
 * [Coiled](https://coiled.io) is a managed Dask service. Get started by reading [Democratizing Satellite Imagery Analysis with Dask](https://coiled.io/blog/democratizing-satellite-imagery-analysis-with-dask/)
 * [Dask with PyTorch for large scale image analysis](https://blog.dask.org/2021/03/29/apply-pretrained-pytorch-model)
 * [dask-geopandas](https://github.com/geopandas/dask-geopandas) -> offers geospatial capabilities of GeoPandas backed by Dask
@@ -1995,6 +1994,7 @@ Flask is often used to serve up a simple web app based on templated HTML files
 * [mapa-streamlit](https://github.com/fgebhart/mapa-streamlit) -> creating 3D-printable models of the earth surface based on mapa
 * [BoulderAreaDetector](https://github.com/pszemraj/BoulderAreaDetector) -> CNN to classify whether a satellite image shows an area would be a good rock climbing spot or not, deployed to streamlit app
 * [streamlit-remotetileserver](https://github.com/banesullivan/streamlit-remotetileserver) -> Easily visualize a remote raster given a URL and check if it is a valid Cloud Optimized GeoTiff (COG)
+* [Streamlit_Image_Sorter](https://github.com/2320sharon/Streamlit_Image_Sorter) -> Generic Image Sorter Interface for Streamlit
 
 ## Julia language
 [Julia](https://julialang.org/) looks and feels a lot like Python, but can be much faster. Julia can call Python, C, and Fortran libraries and is capabale of C/Fortran speeds. Julia can be used in the familiar Jupyterlab notebook environment