Update README.md

README.md

@@ -889,6 +889,8 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [HD-Net](https://github.com/danfenghong/ISPRS_HD-Net) -> High-resolution decoupled network for building footprint extraction via deeply supervised body and boundary decomposition
 
+- [RoofSense](https://github.com/DimitrisMantas/RoofSense/tree/master) -> A novel deep learning solution for the automatic roofing material classification of the Dutch building stock using aerial imagery and laser scanning data fusion
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 ### Segmentation - Solar panels
 
 - [Deep-Learning-for-Solar-Panel-Recognition](https://github.com/saizk/Deep-Learning-for-Solar-Panel-Recognition) -> using both object detection with Yolov5 and Unet segmentation
@@ -1119,6 +1121,10 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [CMTFNet](https://github.com/DrWuHonglin/CMTFNet) -> CMTFNet: CNN and Multiscale Transformer Fusion Network for Remote Sensing Image Semantic Segmentation
 
+- [CM-UNet](https://github.com/XiaoBuL/CM-UNet) -> Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
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+- [Using Stable Diffusion to Improve Image Segmentation Models](https://medium.com/edge-analytics/using-stable-diffusion-to-improve-image-segmentation-models-1e99c25acbf) -> Augmenting Data with Stable Diffusion
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 #
 ## Instance segmentation
 
@@ -2147,6 +2153,8 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [OctaveNet](https://github.com/farhadinima75/OctaveNet) -> An efficient multi-scale pseudo-siamese network for change detection in remote sensing images
 
+- [MaskCD](https://github.com/EricYu97/MaskCD) -> A Remote Sensing Change Detection Network Based on Mask Classification
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 ## Time series
 
@@ -2200,6 +2208,8 @@ The analysis of time series observations in remote sensing data has numerous app
 
 - [hurricane-net](https://github.com/hammad93/hurricane-net) -> A deep learning framework for forecasting Atlantic hurricane trajectory and intensity.
 
+- [CAPES](https://github.com/twin22jw/CAPES/tree/main) -> Construction changes are detected using the U-net model and satellite time series
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 ## Crop classification
 
@@ -2542,6 +2552,10 @@ Note that nearly all the MISR publications resulted from the [PROBA-V Super Reso
 
 - [L1BSR](https://github.com/centreborelli/L1BSR) -> Exploiting Detector Overlap for Self-Supervised SISR of Sentinel-2 L1B Imagery
 
+- [Deep-Harmonization](https://github.com/venkatesh-thiru/Deep-Harmonization) -> Deep Learning-based Harmonization and Super-Resolution of Landsat-8 and Sentinel-2 images
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+- [SGDM](https://github.com/wwangcece/SGDM) -> Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior
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 ### Super-resolution - Miscellaneous
 
 - [The value of super resolution — real world use case](https://medium.com/sentinel-hub/the-value-of-super-resolution-real-world-use-case-2ba811f4cd7f) -> Medium article on parcel boundary detection with super-resolved satellite imagery
@@ -2937,6 +2951,8 @@ Autoencoders are a type of neural network that aim to simplify the representatio
 
 - [satclip](https://github.com/microsoft/satclip) -> A Global, General-Purpose Geographic Location Encoder from Microsoft
 
+- [](https://earthloc-and-earthmatch.github.io/) -> Astronaut Photography Localization & Iterative Coregistration
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 ## Anomaly detection
 Anomaly detection refers to the process of identifying unusual patterns or outliers in satellite or aerial images that do not conform to expected norms. This is crucial in applications such as environmental monitoring, defense surveillance, and urban planning. Machine learning algorithms, particularly unsupervised learning methods, are used to analyze vast amounts of remote sensing data efficiently. These algorithms learn the typical patterns and variations in the data, allowing them to flag anomalies such as unexpected land cover changes, illegal deforestation, or unusual maritime activities. The detection of these anomalies can provide valuable insights for timely decision-making and intervention in various fields.
@@ -3664,6 +3680,8 @@ Training data can be hard to acquire, particularly for rare events such as chang
 
 - [OnlyPlanes](https://github.com/naivelogic/OnlyPlanes) -> dataset and pretrained models for the paper: OnlyPlanes - Incrementally Tuning Synthetic Training Datasets for Satellite Object Detection
 
+- [Using Stable Diffusion to Improve Image Segmentation Models](https://medium.com/edge-analytics/using-stable-diffusion-to-improve-image-segmentation-models-1e99c25acbf) -> Augmenting Data with Stable Diffusion
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 ## Large vision & language models (LLMs & LVMs)