Update README.md

README.md

@@ -165,6 +165,7 @@ In this challenge, you will build a model to classify cloud organization pattern
 * [How to Use Google Earth Engine and Python API to Export Images to Roboflow](https://blog.roboflow.com/how-to-use-google-earth-engine-with-roboflow/) -> to acquire training data
 * [Reduce Satellite Image Resolution with Google Earth Engine](https://towardsdatascience.com/reduce-satellite-image-resolution-with-google-earth-engine-95a129ef488) -> a crucial step before applying machine learning to satellite imagery
 * [ee-fastapi](https://github.com/csaybar/ee-fastapi) is a simple FastAPI web application for performing flood detection using Google Earth Engine in the backend.
+* [How to Download High-Resolution Satellite Data for Anywhere on Earth](https://towardsdatascience.com/how-to-download-high-resolution-satellite-data-for-anywhere-on-earth-5e6dddee2803)
 
 ## Radiant Earth
 * https://www.radiant.earth/
@@ -272,6 +273,8 @@ Whilst classification will assign a label to a whole image, semantic segmentatio
 * [Pixel level segmentation on Azure](https://github.com/Azure/pixel_level_land_classification)
 * [DigitalGlobe article](http://gbdxstories.digitalglobe.com/boats/) - they use a combination classical techniques (masks, erodes) to reduce the search space (identifying water via [NDWI](https://en.wikipedia.org/wiki/Normalized_difference_water_index) which requires SWIR) then apply a binary DL classifier on candidate regions of interest. They deploy the final algo [as a task](https://github.com/platformstories/boat-detector) on their GBDX platform. They propose that in the future an R-CNN may be suitable for the whole process.
 * [Сrор field boundary detection: approaches overview and main challenges](https://soilmate.medium.com/%D1%81r%D0%BE%D1%80-field-boundary-detection-approaches-overview-and-main-challenges-53736725cb06)
+* [A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS](https://www.mdpi.com/2072-4292/13/9/1608) with [code](https://github.com/mnpinto/FireHR)
+
 
 ## Change detection
 Monitor water levels, coast lines, size of urban areas, wildfire damage. Note, clouds change often too..!
@@ -326,6 +329,7 @@ A good introduction to the challenge of performing object detection on aerial im
 * From [this article on sentinelhub](https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13) there are three popular classical algorithms that detects thresholds in multiple bands in order to identify clouds. In the same article they propose using semantic segmentation combined with a CNN for a cloud classifier (excellent review paper [here](https://arxiv.org/pdf/1704.06857.pdf)), but state that this requires too much compute resources.
 * [This article](https://www.mdpi.com/2072-4292/8/8/666) compares a number of ML algorithms, random forests, stochastic gradient descent, support vector machines, Bayesian method.
 * [Segmentation of Clouds in Satellite Images Using Deep Learning](https://medium.com/swlh/segmentation-of-clouds-in-satellite-images-using-deep-learning-a9f56e0aa83d) -> a U-Net is employed to interpret and extract the information embedded in the satellite images in a multi-channel fashion, and finally output a pixel-wise mask indicating the existence of cloud.
+* [Cloud Detection in Satellite Imagery](https://www.azavea.com/blog/2021/02/08/cloud-detection-in-satellite-imagery/) compares FPN and CheapLab architectures on Sentinel-2 L1C and L2A imagery
 
 ## Wealth and economic activity measurement
 The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys
@@ -372,6 +376,9 @@ I group these together since I most often see denoising in the context of SAR im
 * [How to implement augmentations for Multispectral Satellite Images Segmentation using Fastai-v2 and Albumentations](https://towardsdatascience.com/how-to-implement-augmentations-for-multispectral-satellite-images-segmentation-using-fastai-v2-and-ea3965736d1)
 * [Principal Component Analysis: In-depth understanding through image visualization](https://towardsdatascience.com/principal-component-analysis-in-depth-understanding-through-image-visualization-892922f77d9f) applied to Landsat TM images, [with repo](https://github.com/Skumarr53/Principal-Component-Analysis-testing-on-Image-data)
 * [Leveraging Geolocation Data for Machine Learning: Essential Techniques](https://towardsdatascience.com/leveraging-geolocation-data-for-machine-learning-essential-techniques-192ce3a969bc) -> A Gentle Guide to Feature Engineering and Visualization with Geospatial data, in Plain English
+* [3 Tips to Optimize Your Machine Learning Project for Data Labeling](https://www.azavea.com/blog/2020/07/21/3-tips-to-optimize-your-machine-learning-project-for-data-labeling/)
+* [Image Classification Labeling: Single Class versus Multiple Class Projects](https://www.azavea.com/blog/2020/06/08/image-classification-labeling-single-class-versus-multiple-class-projects/)
+* [Labeling Satellite Imagery for Machine Learning](https://www.azavea.com/blog/2020/03/24/labeling-satellite-imagery-for-machine-learning/)
 
 # Miscellaneous (generlly) non ML Techniques
 ## Pansharpening
@@ -445,6 +452,7 @@ A Cloud Optimized GeoTIFF (COG) is a regular GeoTIFF file with an internal organ
 ## STAC - SpatioTemporal Asset Catalog specification
 The STAC specification provides a common metadata specification, API, and catalog format to describe geospatial assets, so they can more easily indexed and discovered. The aim is that the catalogue is crawlable so it can be indexed by a search engine and make imagery discoverable, without requiring yet another API interface. A good place to start is to view the [Planet Disaster Data catalogue](https://planet.stac.cloud/) which has the [catalogue source on Github](https://github.com/cholmes/pdd-stac) and uses the [stac-browser](https://github.com/radiantearth/stac-browser)
 * Spec at https://github.com/radiantearth/stac-spec
+* [Getting Started with STAC APIs](https://www.azavea.com/blog/2021/04/05/getting-started-with-stac-apis/) intro article
 * [SpatioTemporal Asset Catalog API specification](https://github.com/radiantearth/stac-api-spec) -> an API to make geospatial assets openly searchable and crawlable
 * [stacindex](https://stacindex.org/) -> STAC Catalogs, Collections, APIs, Software and Tools
 * Several useful repos on https://github.com/sat-utils
@@ -618,6 +626,7 @@ A conceptually simple approach to serving up deep learning model inference code
 ## Tools for image annotation
 If you are performing object detection you will need to annotate images with bounding boxes. Check that your annotation tool of choice supports large image (likely geotiff) files, as not all will. Note that GeoJSON is widely used by remote sensing researchers but this annotation format is not commonly supported in general computer vision frameworks, and in practice you may have to convert the annotation format to use the data with your chosen framework. There are both closed and open source tools for creating and converting annotation formats.
 * A long list of tools is [here](https://simonwenkel.com/2019/07/19/list-of-annotation-tools-for-machine-learning-research.html)
+* [GroundWork](https://groundwork.azavea.com/) is designed for annotating and labeling geospatial data like satellite imagery, from Azavea
 * [Labelme Image Annotation for Geotiffs](https://medium.com/@wvsharber/labelme-image-annotation-for-geotiffs-b460ba83804f) -> uses [Labelme](https://github.com/wkentaro/labelme)
 * [Label Maker](https://github.com/developmentseed/label-maker) -> downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an `.npz` file for use in machine learning training.
 * [CVAT](https://github.com/openvinotoolkit/cvat) is worth investigating, and have an [open issue](https://github.com/openvinotoolkit/cvat/issues/531) to support large TIFF files. [This article on Roboflow](https://blog.roboflow.com/cvat/) gives a good intro to CVAT.