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Author: robmarkcole

README.md

@@ -2173,6 +2173,12 @@ Crop yield is a crucial metric in agriculture, as it determines the productivity
 
 - [Pixel-based yield mapping and prediction from Sentinel-2 using spectral indices and neural networks](https://www.research-collection.ethz.ch/entities/researchdata/9e080be5-b995-4f47-aa82-44c0d22203a7)
 
+- [Predicting Crop Yield Lows Through Highs via Binned Deep Imbalanced Regression](https://github.com/plant-ai-biophysics-lab/imbalance_deep_regression_yield_forecasting)
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+- [List of CNNs in Agriculture](https://github.com/MohammadElSakka/CNN_in_AGRI)
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+- [DeepYield](https://github.com/kgavahi/DeepYield) -> A combined convolutional neural network with long short-term memory for crop yield forecasting 
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 ## Wealth and economic activity
 
@@ -2347,15 +2353,10 @@ Note that nearly all the MISR publications resulted from the [PROBA-V Super Reso
 
 - [Random Forest Super-Resolution (RFSR repo)](https://github.com/jshermeyer/RFSR) including [sample data](https://github.com/jshermeyer/RFSR/tree/master/SampleImagery)
 
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 - [super-resolution-using-gan](https://github.com/saraivaufc/super-resolution-using-gan) -> Super-Resolution of Sentinel-2 Using Generative Adversarial Networks
 
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 - [SSPSR-Pytorch](https://github.com/junjun-jiang/SSPSR) -> A spatial-spectral prior deep network for single hyperspectral image super-resolution
 
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 - [CinCGAN](https://github.com/Junshk/CinCGAN-pytorch) -> Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
 
 - [Satellite-image-SRGAN using PyTorch](https://github.com/xjohnxjohn/Satellite-image-SRGAN)
@@ -2752,6 +2753,8 @@ Generative networks (e.g. GANs) aim to generate new, synthetic data that appears
 
 - [EarthSynth: Generating Informative Earth Observation with Diffusion Models](https://github.com/jaychempan/EarthSynth)
 
+- [SatDiFuser](https://github.com/yurujaja/SatDiFuser) -> Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?
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 ## Autoencoders, dimensionality reduction, image embeddings & similarity search
 
@@ -2973,6 +2976,8 @@ Mixed data learning is the process of learning from datasets that may contain an
 
 - [methane-emission-project](https://github.com/stlbnmaria/methane-emission-project) -> Classification CNNs was combined in an ensemble approach with traditional methods on tabular data
 
+- [AutoMergeNet](https://github.com/ADA-research/AutoMergeNet) -> a neural architecture search approach for automatic methane plume detection in TROPOMI images
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 ## Few & zero shot learning
 This is a class of techniques which attempt to make predictions for classes with few, one or even zero examples provided during training. In zero shot learning (ZSL) the model is assisted by the provision of auxiliary information which typically consists of descriptions/semantic attributes/word embeddings for both the seen and unseen classes at train time ([ref](https://learnopencv.com/zero-shot-learning-an-introduction/)). These approaches are particularly relevant to remote sensing, where there may be many examples of common classes, but few or even zero examples for other classes of interest.