Update README.md

README.md

@@ -223,7 +223,6 @@ Several different techniques can be used to count the number of objects in an im
 * [kaggle-airbus-ship-detection-challenge](https://github.com/toshi-k/kaggle-airbus-ship-detection-challenge) -> using oriented SSD
 * [shipsnet-detector](https://github.com/rhammell/shipsnet-detector) -> Detect container ships in Planet imagery using machine learning
 * [Classifying Ships in Satellite Imagery with Neural Networks](https://towardsdatascience.com/classifying-ships-in-satellite-imagery-with-neural-networks-944024879651) -> applied to the Kaggle Ships in Satellite Imagery dataset
-* [Ship Detection in Satellite Images using Mask R-CNN](https://spell.ml/blog/ship-detection-in-satellite-images-using-mask-r-cnn-XyMA4xEAACUAb9G1) blog post by spell.ml
 * [Mask R-CNN for Ship Detection & Segmentation](https://medium.com/@gabogarza/mask-r-cnn-for-ship-detection-segmentation-a1108b5a083) blog post with [repo](https://github.com/gabrielgarza/Mask_RCNN)
 * [contrastive_SSL_ship_detection](https://github.com/alina2204/contrastive_SSL_ship_detection) -> Contrastive self supervised learning for ship detection in Sentinel 2 images
 * [Boat detection with multi-region-growing method in satellite images](https://medium.com/@ipmach/boat-detection-with-multi-region-growing-method-in-satellite-images-3339a6c29a8c)
@@ -967,7 +966,7 @@ A GPU is required for training deep learning models (but not necessarily for inf
 * Whilst the combo of python and keras/tensorflow/pytorch are currently preeminent, new python libraries such as [Jax](https://github.com/google/jax) and alternative languages such as [Julia](https://julialang.org/) are showing serious promise
 
 # Cloud providers
-An overview of the most relevant services provided by AWS, Google and Microsoft. Also consider one of the many smaller but more specialised platorms such as [spell.ml](https://spell.ml/) or [paperspace](https://www.paperspace.com/)
+An overview of the most relevant services provided by AWS, Google and Microsoft. Also consider one of the many smaller but more specialised platorms such as [paperspace](https://www.paperspace.com/)
 
 ## AWS
 * Host your data on [S3](https://aws.amazon.com/s3/) and metadata in a db such as [postgres](https://aws.amazon.com/rds/postgresql/)
@@ -1162,7 +1161,7 @@ Arguably the most significant paid software for working with maps and geographic
 * [dl-satellite-docker](https://github.com/sshuair/dl-satellite-docker) -> docker files for geospatial analysis, including tensorflow, pytorch, gdal, xgboost...
 * [AIDE V2 - Tools for detecting wildlife in aerial images using active learning](https://github.com/microsoft/aerial_wildlife_detection)
 * [Land Cover Mapping web app from Microsoft](https://github.com/microsoft/landcover)
-* [Solaris](https://github.com/CosmiQ/solaris) -> An open source ML pipeline for overhead imagery by [CosmiQ Works](https://www.cosmiqworks.org/), similar to Rastervision but with some unique very vool features. Also checkout [lunular](https://github.com/rbavery/lunular) which is a fork of Solaris which extracts the dataset preprocessing and evaluation methods that do not depend on tensorflow or pytorch
+* [Solaris](https://github.com/CosmiQ/solaris) -> An open source ML pipeline for overhead imagery by [CosmiQ Works](https://www.cosmiqworks.org/), similar to Rastervision but with some unique very vool features
 * [openSAR](https://github.com/EarthBigData/openSAR) -> Synthetic Aperture Radar (SAR) Tools and Documents from Earth Big Data LLC (http://earthbigdata.com/)
 * [qhub](https://qhub.dev) -> QHub enables teams to build and maintain a cost effective and scalable compute/data science platform in the cloud.
 * [imagej](https://imagej.net) -> a very versatile image viewer and processing program
@@ -1359,6 +1358,7 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [Kaleido](https://github.com/plotly/Kaleido) -> Fast static image export for web-based visualization libraries with zero dependencies
 * [flask-vector-tiles](https://github.com/mblackgeo/flask-vector-tiles) -> A simple Flask/leaflet based webapp for rendering vector tiles from PostGIS
 * [Embedding Projector in Wandb](https://docs.wandb.ai/ref/app/features/panels/weave/embedding-projector) -> allows users to plot multi-dimensional embeddings on a 2D plane using common dimension reduction algorithms like PCA, UMAP, and t-SNE
+* [flask-geocoding-webapp](https://github.com/mblackgeo/flask-geocoding-webapp) -> A quick example Flask application for geocoding and rendering a webmap using Folium/Leaflet
 
 ## 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
@@ -1405,6 +1405,7 @@ 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
 * [pmapper](https://github.com/nasa-jpl/pmapper) -> a super-resolution and deconvolution toolkit for python. PMAP stands for Poisson Maximum A-Posteriori, a highly flexible and adaptable algorithm for these problems
+* [pylandtemp](https://github.com/pylandtemp/pylandtemp) -> Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python
 
 ## 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