move State of the art engineering

README.md

@@ -6,9 +6,9 @@ This document lists resources for performing deep learning (DL) on satellite ima
 * [ML best practice](https://github.com/robmarkcole/satellite-image-deep-learning#ml-best-practice)
 * [ML metrics](https://github.com/robmarkcole/satellite-image-deep-learning#ml-metrics)
 * [Datasets](https://github.com/robmarkcole/satellite-image-deep-learning#datasets)
-* [State of the art engineering](https://github.com/robmarkcole/satellite-image-deep-learning#state-of-the-art-engineering)
 * [Online platforms for analytics](https://github.com/robmarkcole/satellite-image-deep-learning#online-platforms-for-analytics)
 * [Free online compute](https://github.com/robmarkcole/satellite-image-deep-learning#free-online-compute)
+* [State of the art engineering](https://github.com/robmarkcole/satellite-image-deep-learning#state-of-the-art-engineering)
 * [Cloud providers](https://github.com/robmarkcole/satellite-image-deep-learning#cloud-providers)
 * [Deploying models](https://github.com/robmarkcole/satellite-image-deep-learning#deploying-models)
 * [Image formats, data management and catalogues](https://github.com/robmarkcole/satellite-image-deep-learning#image-formats-data-management-and-catalogues)
@@ -781,16 +781,6 @@ Since there is a whole community around GEE I will not reproduce it here but lis
 * [BlenderGIS](https://github.com/domlysz/BlenderGIS) could be used for synthetic data generation
 * [bifrost.ai](https://www.bifrost.ai/) -> simulated data service with geospatial output data formats
 
-# State of the art engineering
-* Compute and data storage are moving to the cloud. Read how [Planet](https://cloud.google.com/customers/planet) and [Airbus](https://cloud.google.com/customers/airbus) use the cloud
-* Google Earth Engine and Microsoft Planetary Computer are democratising access to massive compute platforms
-* No-code platforms and auto-ml are making ML techniques more accessible than ever
-* Custom hardware is being developed for rapid training and inferencing with deep learning models, both in the datacenter and at the edge
-* Supervised ML methods typically require large annotated datasets, but approaches such as self-supervised and active learning are offering alternatives pathways
-* Traditional data formats aren't designed for processing on the cloud, so new standards are evolving such as COGS and STAC
-* Computer vision traditionally delivered high performance image processing on a CPU by using compiled languages like C++, as used by OpenCV for example. The advent of GPUs are changing the paradigm, with alternatives optimised for GPU being created, such as [Kornia](https://kornia.github.io/)
-* 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
-
 # Online platforms for analytics
 * [This article discusses some of the available platforms](https://medium.com/pangeo/cloud-native-geoprocessing-of-earth-observation-satellite-data-with-pangeo-997692d91ca2)
 * [Pangeo](http://pangeo.io/index.html) -> There is no single software package called “pangeo”; rather, the Pangeo project serves as a coordination point between scientists, software, and computing infrastructure. Includes open source resources for parallel processing using Dask and Xarray. Pangeo recently announced their 2.0 goals: pivoting away from directly operating cloud-based JupyterHubs, and towards eductaion and research
@@ -820,6 +810,16 @@ A GPU is required for training deep learning models (but not necessarily for inf
 * Tensorflow, pytorch & fastai available but you may need to update them
 * Advantage that many datasets are already available
 
+# State of the art engineering
+* Compute and data storage are moving to the cloud. Read how [Planet](https://cloud.google.com/customers/planet) and [Airbus](https://cloud.google.com/customers/airbus) use the cloud
+* Google Earth Engine and Microsoft Planetary Computer are democratising access to massive compute platforms
+* No-code platforms and auto-ml are making ML techniques more accessible than ever
+* Custom hardware is being developed for rapid training and inferencing with deep learning models, both in the datacenter and at the edge
+* Supervised ML methods typically require large annotated datasets, but approaches such as self-supervised and active learning are offering alternatives pathways
+* Traditional data formats aren't designed for processing on the cloud, so new standards are evolving such as COGS and STAC
+* Computer vision traditionally delivered high performance image processing on a CPU by using compiled languages like C++, as used by OpenCV for example. The advent of GPUs are changing the paradigm, with alternatives optimised for GPU being created, such as [Kornia](https://kornia.github.io/)
+* 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 and Google. Also consider Microsoft Azure, or one of the many smaller but more specialised platorms such as [spell.ml](https://spell.ml/) or [paperspace](https://www.paperspace.com/)