various

Author: robmarkcole

README.md

@@ -1574,6 +1574,8 @@ Regression in remote sensing involves predicting continuous variables such as wi
 
 - [Seabed-Net](https://github.com/pagraf/Seabed-Net) -> A multi-task network for joint bathymetry and pixel-based seabed classification from remote sensing imagery in shallow waters, uses [MagicBathyNet](https://www.magicbathy.eu/magicbathynet.html) dataset
 
+- [ECHOSAT](https://github.com/AI4Forest/ECHOSAT) -> Estimating Canopy Height Over Space And Time, uses a Swin Video UNet architecture that processes multi-sensor satellite data.
+
 #
 ## Cloud detection & removal
 
@@ -2018,6 +2020,8 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [ChangeDINO](https://github.com/chingheng0808/ChangeDINO) -> DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery.
 
+- [mason_cd](https://github.com/blaz-r/mason_cd) -> Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations
+
 #
 ## Time series
 
@@ -2189,6 +2193,8 @@ Crop yield is a crucial metric in agriculture, as it determines the productivity
 
 - [VITA](https://github.com/neehan/VITA) -> Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting
 
+- [Bayesian-posterior-based-EnKF](https://github.com/paperoses/Bayesian-posterior-based-EnKF) -> The improved winter wheat yield estimation by assimilating GLASS LAI into a crop growth model with the proposed Bayesian posterior-based ensemble Kalman filter.
+
 #
 ## Wealth and economic activity
 
@@ -2882,6 +2888,10 @@ Autoencoders are a type of neural network that aim to simplify the representatio
 
 - [pixelverse](https://github.com/developmentseed/pixelverse) -> Cloud native tooling to generate and store pixelwise geospatial foundation model embeddings.
 
+- [Looted Site Detection](https://github.com/microsoft/looted_site_detection) -> compares embeddings with CNNs, by Microsoft
+
+- [AI-for-Good-Tutorial-2026](https://github.com/embed2scale/AI-for-Good-Tutorial-2026) -> introduces embedding workflows for Earth Observation using TerraTorch and NeuCo-Bench
+
 #
 ## 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.
@@ -3695,7 +3705,7 @@ Explainable AI (XAI) is a field of artificial intelligence that focuses on devel
 
 - [Clay Foundation Model](https://github.com/Clay-foundation/model) -> an open source AI model and interface for Earth.
 
-- [TerraTorch](https://github.com/IBM/terratorch) -> a Python toolkit for fine-tuning Geospatial Foundation Models from IBM, based on PyTorch Lightning and TorchGeo
+- [TerraTorch](https://github.com/terrastackai/terratorch) -> a Python toolkit for fine-tuning Geospatial Foundation Models from IBM, based on PyTorch Lightning and TorchGeo
 
 - [EarthPT](https://github.com/aspiaspace/earthPT) -> A time series foundation model for Earth Observation