Update README.md

README.md

@@ -472,6 +472,8 @@ Semantic Segmentation
 * [electrical_substation_detection](https://github.com/thisishardik/electrical_substation_detection) -> using UNet, Albumentations for image augmentation, and OpenCV for computer vision tasks
 * [PLGAN-for-Power-Line-Segmentation](https://github.com/R3ab/PLGAN-for-Power-Line-Segmentation) -> code for 2022 [paper](https://arxiv.org/abs/2204.07243): PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images
 * [MCAN-OilSpillDetection](https://github.com/liyongqingupc/MCAN-OilSpillDetection) -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training, with [paper](https://www.mdpi.com/2072-4292/13/12/2378). A multiscale conditional adversarial network (MCAN) trained with four oil spill observation images accurately detects oil spills in new images.
+* [plastics](https://github.com/earthrise-media/plastics) -> Detecting and Monitoring Plastic Waste Aggregations in Sentinel-2 Imagery for [globalplasticwatch.org](https://globalplasticwatch.org/)
+* [mining-detector](https://github.com/earthrise-media/mining-detector) -> detection of artisanal gold mines in Sentinel-2 satellite imagery for [Amazon Mining Watch](https://amazonminingwatch.org/). Also covers clandestine airstrips
 
 ### Instance segmentation
 In instance segmentation, each individual 'instance' of a segmented area is given a unique lable. For detection of very small objects this may a good approach, but it can struggle seperating individual objects that are closely spaced.
@@ -1255,6 +1257,7 @@ GANS are famously used for generating synthetic data, see the section [Synthetic
 * [Remote-Sensing-RVSA](https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA) -> code for 2022 [paper](https://arxiv.org/abs/2208.03987): Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
 * [SatViT](https://github.com/antofuller/SatViT) -> self-supervised training of multispectral optical and SAR vision transformers
 * [UDA_for_RS](https://github.com/Levantespot/UDA_for_RS) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/19/4942): Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer
+* [Vision Transformers for Low Earth Orbit Satellites](https://myrtle.ai/learn/leo-1-low-earth-orbit-satellites/) -> blog post that investigates deploying Vision Transformers on low earth orbit satellites
 
 </p>
 </details>