Update README.md

README.md

@@ -258,6 +258,7 @@ Generally treated as a semantic segmentation problem.
 * [Multitemporal Cloud Masking in Google Earth Engine](https://github.com/IPL-UV/ee_ipl_uv)
 * [s2cloudmask](https://github.com/daleroberts/s2cloudmask) -> Sentinel-2 Cloud and Shadow Detection using Machine Learning
 * [sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector) -> Sentinel Hub Cloud Detector for Sentinel-2 images in Python
+* [dsen2-cr](https://github.com/ameraner/dsen2-cr) -> cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion, contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset
 
 ## Change detection & time-series
 Monitor water levels, coast lines, size of urban areas, wildfire damage. Note, clouds change often too..!
@@ -308,6 +309,7 @@ The goal is to predict economic activity from satellite imagery rather than cond
 * [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)
 * [Building a Spatial Model to Classify Global Urbanity Levels](https://towardsdatascience.com/building-a-spatial-model-to-classify-global-urbanity-levels-e2fb9da7252) -> estimage global urbanity levels from population data, nightime lights and road networks
 * [deeppop](https://deeppop.github.io/) -> Deep Learning Approach for Population Estimation from Satellite Imagery, also [on Github](https://github.com/deeppop)
+* [Estimating telecoms demand in areas of poor data availability](https://github.com/edwardoughton/taddle) -> with papers on [arxiv](https://arxiv.org/abs/2006.07311) and [Science Direct](https://www.sciencedirect.com/science/article/abs/pii/S0736585321000617)
 
 ## Super-resolution
 Super-resolution attempts to enhance the resolution of an imaging system, and can be applied as a pre-processing step to improve the detection of small objects. For an introduction to this topic [read this excellent article](https://bleedai.com/super-resolution-going-from-3x-to-8x-resolution-in-opencv/). Note that super resolution techniques are generally grouped into single image super resolution (SISR) **or** a multi image super resolution (MISR) which is typically applied to video frames.
@@ -1140,7 +1142,7 @@ So improtant this pair gets their own section. GDAL is THE command line tool for
 * [rasterstats](https://pythonhosted.org/rasterstats/) -> summarize geospatial raster datasets based on vector geometries
 * [turfpy](https://turfpy.readthedocs.io/en/latest/index.html) -> a Python library for performing geospatial data analysis which reimplements turf.js
 * [image-similarity-measures](https://github.com/up42/image-similarity-measures) -> Implementation of eight evaluation metrics to access the similarity between two images. [Blog post here](https://up42.com/blog/tech/image-similarity-measures)
-* [rsgislib](https://github.com/remotesensinginfo/rsgislib) -> Remote Sensing and GIS Software Library; python module tools for processing spatial data.
+* [rsgislib](https://github.com/remotesensinginfo/rsgislib) -> Remote Sensing and GIS Software Library; python module tools for processing spatial and image data.
 * [eo-learn](https://eo-learn.readthedocs.io/en/latest/index.html) is a collection of open source Python packages that have been developed to seamlessly access and process spatio-temporal image sequences acquired by any satellite fleet in a timely and automatic manner
 * [RStoolbox: Tools for Remote Sensing Data Analysis in R](https://bleutner.github.io/RStoolbox/)
 * [nd](https://github.com/jnhansen/nd) -> Framework for the analysis of n-dimensional, multivariate Earth Observation data, built on xarray
@@ -1242,6 +1244,7 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [sentinelsat](https://github.com/sentinelsat/sentinelsat) -> Search and download Copernicus Sentinel satellite images
 * [landsatxplore](https://github.com/yannforget/landsatxplore) -> Search and download Landsat scenes from EarthExplorer
 * [OpenSarToolkit](https://github.com/ESA-PhiLab/OpenSarToolkit) -> High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the python language
+* [lsru](https://github.com/loicdtx/lsru) -> Query and Order Landsat Surface Reflectance data via ESPA
 
 ## OpenStreetMap
 [OpenStreetMap](https://www.openstreetmap.org/) (OSM) is a map of the world, created by people like you and free to use under an open license. Quite a few publications use OSM data for annotations & ground truth. Note that the data is created by volunteers and the quality can be variable
@@ -1276,6 +1279,7 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [mapboxgl-jupyter](https://github.com/mapbox/mapboxgl-jupyter) -> Use Mapbox GL JS to visualize data in a Python Jupyter notebook
 * [cartoframes](https://github.com/CartoDB/cartoframes) -> integrate CARTO maps, analysis, and data services into data science workflows
 * [datashader](https://datashader.org/) -> create meaningful representations of large datasets quickly and flexibly. Read [Creating Visual Narratives from Geospatial Data Using Open-Source Technology Maxar blog post](https://blog.maxar.com/tech-and-tradecraft/2021/creating-visual-narratives-from-geospatial-data-using-open-source-technology)
+* [Kaleido](https://github.com/plotly/Kaleido) -> Fast static image export for web-based visualization libraries with zero dependencies
 
 ## Streamlit
 [Streamlit](https://streamlit.io/) is an awesome python framework for creating apps with python. Additionally they will host the apps free of charge. Here I list resources which are EO related. Note that a component is an addon which extends Streamlits basic functionality
@@ -1316,6 +1320,8 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [awesome-spectral-indices](https://github.com/davemlz/awesome-spectral-indices) -> A ready-to-use curated list of Spectral Indices for Remote Sensing applications
 * [urban-footprinter](https://github.com/martibosch/urban-footprinter) -> A convolution-based approach to detect urban extents from raster datasets
 * [ocean_color](https://github.com/marrs-lab/ocean_color) -> Tools and algorithms for drone and satellite based ocean color science
+* [poliastro](https://github.com/poliastro/poliastro) -> pure Python library for interactive Astrodynamics and Orbital Mechanics, with a focus on ease of use, speed, and quick visualization
+* [acolite](https://github.com/acolite/acolite) -> generic atmospheric correction module
 
 # Movers and shakers on Github
 * [Adam Van Etten](https://github.com/avanetten) is doing interesting things in object detection and segmentation