Update README.md

README.md

@@ -298,7 +298,7 @@ A good introduction to the challenge of performing object detection on aerial im
 * [Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN)](https://github.com/avanetten/simrdwn) -> combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery
 * Several useful articles on [awesome-tiny-object-detection](https://github.com/kuanhungchen/awesome-tiny-object-detection)
 * [Challenges with SpaceNet 4 off-nadir satellite imagery: Look angle and target azimuth angle](https://medium.com/the-downlinq/challenges-with-spacenet-4-off-nadir-satellite-imagery-look-angle-and-target-azimuth-angle-2402bc4c3cf6) -> building prediction in images taken at nearly identical look angles — for example, 29 and 30 degrees — produced radically different performance scores.
-* [YOLTv4](https://github.com/avanetten/yoltv4) -> YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks.
+* [YOLTv4](https://github.com/avanetten/yoltv4) -> YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks. Read [Announcing YOLTv4: Improved Satellite Imagery Object Detection](https://towardsdatascience.com/announcing-yoltv4-improved-satellite-imagery-object-detection-f5091e913fad)
 * [Benchmarks for Object Detection in Aerial Images](https://github.com/dingjiansw101/AerialDetection) -> codebase created to build benchmarks for object detection in aerial images
 
 #### Object detection - buildings
@@ -359,6 +359,7 @@ Super-resolution imaging is a class of techniques that enhance the resolution of
 Generative Adversarial Networks, or GANS, can be used to translate images, e.g. from SAR to RGB.
 * [How to Develop a Pix2Pix GAN for Image-to-Image Translation](https://machinelearningmastery.com/how-to-develop-a-pix2pix-gan-for-image-to-image-translation/) -> how to develop a Pix2Pix model for translating satellite photographs to Google map images. A good intro to GANS
 * [SAR to RGB Translation using CycleGAN](https://www.esri.com/arcgis-blog/products/api-python/imagery/sar-to-rgb-translation-using-cyclegan/) -> uses a CycleGAN model in the ArcGIS API for Python
+* [A growing problem of ‘deepfake geography’: How AI falsifies satellite images](https://www.washington.edu/news/2021/04/21/a-growing-problem-of-deepfake-geography-how-ai-falsifies-satellite-images/)
 
 ## SAR & Denoising
 I group these together since I most often see denoising in the context of SAR imagery.
@@ -482,7 +483,7 @@ What are companies doing?
 * Planet have a [Jupyter notebook platform](https://developers.planet.com/) which can be deployed locally.
 * [jupyteo.com](https://www.jupyteo.com) -> hosted Jupyter environment with many features for working with EO data
 * [eurodatacube.com](https://eurodatacube.com) -> data & platform for EO analytics in Jupyter env, paid
-* [Unfolded Studio](https://studio.unfolded.ai/) -> next generation geospatial analytics and visualization platform building on open source geospatial technologies including kepler.gl, deck.gl and H3
+* [Unfolded Studio](https://studio.unfolded.ai/) -> next generation geospatial analytics and visualization platform building on open source geospatial technologies including kepler.gl, deck.gl and H3. Processing is down browser side enabling excellent performance. [Rasters support added April 2021](https://www.unfolded.ai/blog/2021-04-28-raster-layer/)
 * [up42](https://up42.com/) is a developer platform and marketplace, offering all the building blocks for powerful, scalable geospatial products
 * [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/) -> direct Google Earth Engine competitor in the making?