Update README.md

README.md

@@ -30,7 +30,9 @@ Orbuculum's potential extends far beyond the reinvention of the GIS/EO research
 * 📺 [Conversation between Robin and Derek Ding, the co-founder of the Orbuculum platform](https://youtu.be/pDduLbMwSe8)
 * 🗣️ [Orbuculum Discord](https://t.co/4JOkajHZ0l)
 * 💻 [Orbuculum Github](https://github.com/Orbuculum-DAO)
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 * 🐦 [Orbuculum Twitter](https://twitter.com/Orbuculum_xyz)
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@@ -84,7 +86,7 @@ Orbuculum's potential extends far beyond the reinvention of the GIS/EO research
   <b>The UC merced dataset is a well known classification dataset.</b>
 </p>
 
-Classification is a fundamental task in remote sensing data analysis, where the goal is to assign a semantic label to each image, such as 'urban', 'forest', 'agricultural land', etc. The process of assigning labels to an image is known as image-level classification. However, in some cases, a single image might contain multiple different land cover types, such as a forest with a river running through it, or a city with both residential and commercial areas. In these cases, image-level classification becomes more complex and involves assigning multiple labels to a single image. This can be accomplished using a combination of feature extraction and machine learning algorithms to accurately identify the different land cover types. It is important to note that image-level classification should not be confused with pixel-level classification, also known as semantic segmentation. While image-level classification assigns a single label to an entire image, semantic segmentation assigns a label to each individual pixel in an image, resulting in a highly detailed and accurate representation of the land cover types in an image. Read [A brief introduction to satellite image classification with neural networks](https://www.satellite-image-deep-learning.com/p/a-brief-introduction-to-satellite)
+Classification is a fundamental task in remote sensing data analysis, where the goal is to assign a semantic label to each image, such as 'urban', 'forest', 'agricultural land', etc. The process of assigning labels to an image is known as image-level classification. However, in some cases, a single image might contain multiple different land cover types, such as a forest with a river running through it, or a city with both residential and commercial areas. In these cases, image-level classification becomes more complex and involves assigning multiple labels to a single image. This can be accomplished using a combination of feature extraction and machine learning algorithms to accurately identify the different land cover types. It is important to note that image-level classification should not be confused with pixel-level classification, also known as semantic segmentation. While image-level classification assigns a single label to an entire image, semantic segmentation assigns a label to each individual pixel in an image, resulting in a highly detailed and accurate representation of the land cover types in an image. Read [A brief introduction to satellite image classification with neural networks](https://medium.com/@robmarkcole/a-brief-introduction-to-satellite-image-classification-with-neural-networks-3ce28be15683)
 
    1.1. Land classification on Sentinel 2 data using a [simple sklearn cluster algorithm](https://github.com/acgeospatial/Satellite_Imagery_Python/blob/master/Clustering_KMeans-Sentinel2.ipynb) or [deep learning CNN](https://towardsdatascience.com/land-use-land-cover-classification-with-deep-learning-9a5041095ddb) `BEGINNER`
 
@@ -243,7 +245,7 @@ Classification is a fundamental task in remote sensing data analysis, where the
   <b>(left) a satellite image and (right) the semantic classes in the image.</b>
 </p>
 
-Image segmentation is a crucial step in image analysis and computer vision, with the goal of dividing an image into semantically meaningful segments or regions. The process of image segmentation assigns a class label to each pixel in an image, effectively transforming an image from a 2D grid of pixels into a 2D grid of pixels with assigned class labels. One common application of image segmentation is road or building segmentation, where the goal is to identify and separate roads and buildings from other features within an image. To accomplish this task, single class models are often trained to differentiate between roads and background, or buildings and background. These models are designed to recognize specific features, such as color, texture, and shape, that are characteristic of roads or buildings, and use this information to assign class labels to the pixels in an image. Another common application of image segmentation is land use or crop type classification, where the goal is to identify and map different land cover types within an image. In this case, multi-class models are typically used to recognize and differentiate between multiple classes within an image, such as forests, urban areas, and agricultural land. These models are capable of recognizing complex relationships between different land cover types, allowing for a more comprehensive understanding of the image content. Read [A brief introduction to satellite image segmentation with neural networks](https://www.satellite-image-deep-learning.com/p/a-brief-introduction-to-satellite-365). **Note** that many articles which refer to 'hyperspectral land classification' are often actually describing semantic segmentation. [Image source](https://towardsdatascience.com/semantic-segmentation-of-aerial-imagery-using-u-net-in-python-552705238514)
+Image segmentation is a crucial step in image analysis and computer vision, with the goal of dividing an image into semantically meaningful segments or regions. The process of image segmentation assigns a class label to each pixel in an image, effectively transforming an image from a 2D grid of pixels into a 2D grid of pixels with assigned class labels. One common application of image segmentation is road or building segmentation, where the goal is to identify and separate roads and buildings from other features within an image. To accomplish this task, single class models are often trained to differentiate between roads and background, or buildings and background. These models are designed to recognize specific features, such as color, texture, and shape, that are characteristic of roads or buildings, and use this information to assign class labels to the pixels in an image. Another common application of image segmentation is land use or crop type classification, where the goal is to identify and map different land cover types within an image. In this case, multi-class models are typically used to recognize and differentiate between multiple classes within an image, such as forests, urban areas, and agricultural land. These models are capable of recognizing complex relationships between different land cover types, allowing for a more comprehensive understanding of the image content. Read [A brief introduction to satellite image segmentation with neural networks](https://medium.com/@robmarkcole/a-brief-introduction-to-satellite-image-segmentation-with-neural-networks-33ea732d5bce). **Note** that many articles which refer to 'hyperspectral land classification' are often actually describing semantic segmentation. [Image source](https://towardsdatascience.com/semantic-segmentation-of-aerial-imagery-using-u-net-in-python-552705238514)
 
 ### 2.1. Segmentation - Land use & land cover
 
@@ -699,7 +701,7 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
   2.9.13. [spacenet_building_detection](https://github.com/motokimura/spacenet_building_detection) solution by [motokimura](https://github.com/motokimura) using Unet
 
-  2.9.14. [How to extract building footprints from satellite images using deep learning](https://azure.microsoft.com/en-gb/blog/how-to-extract-building-footprints-from-satellite-images-using-deep-learning/)
+  2.9.14. [How to extract building footprints from satellite images using deep learning](https://azure.microsoft.com/en-us/blog/how-to-extract-building-footprints-from-satellite-images-using-deep-learning/)
 
   2.9.15. [Vec2Instance](https://github.com/lakmalnd/Vec2Instance) -> applied to the SpaceNet challenge AOI 2 (Vegas) building footprint dataset, tensorflow v1.12
 
@@ -2243,7 +2245,7 @@ Remote sensing images are used in disaster response to identify and assess damag
 
   13.11. [BDD-Net](https://github.com/jinyuan30/Recognize-damaged-buildings) -> code for 2020 paper: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery
 
-  13.12. [Automatic_Disaster_Detection](https://github.com/yoji-kuretake-like/Automatic_Disaster_Detection) -> detect the affected area by natural disasters by using the way of semantic segmentation and change detection method
+  13.12. [building-segmentation-disaster-resilience](https://github.com/kbrodt/building-segmentation-disaster-resilience) -> 2nd place solution in the Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience
 
   13.13. [Flooding Damage Detection from Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks](https://github.com/weining20000/Flooding-Damage-Detection-from-Post-Hurricane-Satellite-Imagery-Based-on-CNN)
 
@@ -2269,8 +2271,6 @@ Remote sensing images are used in disaster response to identify and assess damag
 
   13.24. [DAHiTra](https://github.com/nka77/DAHiTra) -> code for 2022 [paper](https://arxiv.org/abs/2208.02205): Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images. Uses xView2 xBD dataset
 
-  13.25. [building-segmentation-disaster-resilience](https://github.com/kbrodt/building-segmentation-disaster-resilience) -> 2nd place solution in the Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience
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 ## 14. Super-resolution