diff --git a/README.md b/README.md
index 7a850f19..65c5643b 100644
--- a/README.md
+++ b/README.md
@@ -473,6 +473,10 @@ Image registration is the process of transforming different sets of data into on
 * Kornia provides [image registration by gradient decent](https://kornia-tutorials.readthedocs.io/en/latest/image_registration.html)
 * [LoFTR](https://github.com/zju3dv/LoFTR) -> Detector-Free Local Feature Matching with Transformers. Good performance matching satellite image pairs, tryout the web demo on your data
 
+## Multi-sensor/multi-modal fusion
+* [CropTypeMapping](https://github.com/ellaampy/CropTypeMapping) -> Crop type mapping from optical and radar (Sentinel-1&2) time series using attention-based deep learning
+* [Multimodal-Remote-Sensing-Toolkit](https://github.com/likyoo/Multimodal-Remote-Sensing-Toolkit) -> uses Hyperspectral and LiDAR Data
+
 ## Object tracking
 * [Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker](https://arxiv.org/abs/1804.09323) arxiv paper
 
@@ -1059,6 +1063,7 @@ For supervised machine learning, you will require annotated images. For example
 * [hasty.ai](https://hasty.ai/) -> supports model assisted annotation & inferencing
 * TensorFlow Object Detection API provides a [handy utility](https://github.com/tensorflow/models/blob/6a55ecdea7afda51f9dc42dc17104bd6444395d9/research/object_detection/utils/colab_utils.py#L384) for object annotation within Google Colab notebooks. See usage [here](https://github.com/yasserius/tf2-object-detection-api#label-images-in-colab)
 * [coco-annotator](https://github.com/jsbroks/coco-annotator)
+* [pylabel](https://github.com/pylabel-project/pylabel) -> Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo. PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model
 
 ## EO specific annotation tools
 Also check the section **Image handling, manipulation & dataset creation**
@@ -1331,6 +1336,20 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [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
 
+## 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
+* [Why you should invest in Julia now, as a Data Scientist](https://medium.com/@logankilpatrick/why-you-should-invest-in-julia-now-as-a-data-scientist-30dc346d62e4)
+* [eBook: Introduction to Datascience with Julia](https://datascience-book.gitlab.io/)
+* [Flux.jl](https://github.com/FluxML/Flux.jl) -> the ML library that doesn't make you tensor. Checkout [The Deep Learning with Julia book](https://github.com/logankilpatrick/DeepLearningWithJulia)
+* [GDAL.jl](https://github.com/JuliaGeo/GDAL.jl) -> Thin Julia wrapper for GDAL
+* [GeoInterface.jl](https://github.com/JuliaGeo/GeoInterface.jl) -> A Julia Protocol for Geospatial Data
+* [JuliaImages: image processing and machine vision for Julia](https://juliaimages.org/stable/)
+* [MLJ.jl](https://github.com/alan-turing-institute/MLJ.jl) -> A Julia machine learning framework
+* [RemoteS.jl](https://github.com/GenericMappingTools/RemoteS.jl) -> Remote sensing data processing
+* [SatelliteToolbox.jl](https://github.com/JuliaSpace/SatelliteToolbox.jl) -> This package contains several functions to build simulations related with satellites
+* [SatelliteDynamics.jl](https://github.com/sisl/SatelliteDynamics.jl) -> a satellite dynamics modeling package
+* [Sentinel.jl](https://github.com/mhudecheck/Sentinel.jl) -> library for processing ESA Sentinel 2 satellite data
+
 # Movers and shakers on Github
 * [Adam Van Etten](https://github.com/avanetten) is doing interesting things in object detection and segmentation
 * [Andrew Cutts](https://github.com/acgeospatial) cohosts the [Scene From Above podcast](https://scenefromabove.podbean.com) and has many interesting repos
@@ -1411,6 +1430,9 @@ For a full list of companies, on and off Github, checkout [awesome-geospatial-co
 * I highly recommend [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python-second-edition) by François Chollet
 * [Practical Deep Learning for Cloud, Mobile & Edge](https://github.com/PracticalDL/Practical-Deep-Learning-Book)
 
+# Podcasts
+* [The Scene From Above Podcast](https://scenefromabove.podbean.com/)
+
 # Online communities
 * [fast AI geospatial study group](https://forums.fast.ai/t/geospatial-deep-learning-resources-study-group/31044)
 * [Kaggle Intro to Satellite imagery Analysis group](https://www.kaggle.com/getting-started/131455)