Update README.md

Author: robmarkcole

README.md

@@ -349,6 +349,9 @@ Image segmentation is a crucial step in image analysis and computer vision, with
 
 - [LoveNAS](https://github.com/Junjue-Wang/LoveNAS) -> LoveNAS: Towards Multi-Scene Land-Cover Mapping via Hierarchical Searching Adaptive Network
 
+- [FLAIR-2 challenge](https://github.com/IGNF/FLAIR-2) -> Semantic segmentation and domain adaptation challenge proposed by the French National Institute of Geographical and Forest Information (IGN)
+
+- [flair-2 8th place solution](https://github.com/association-rosia/flair-2)
 
 ### Segmentation - Vegetation, deforestation, crops & crop boundaries
 
@@ -544,6 +547,8 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [MECNet](https://github.com/zhilyzhang/MECNet) -> Rich CNN features for water-body segmentation from very high resolution aerial and satellite imagery
 
+- [SWRNET](https://github.com/trongan93/swrnet) -> A Deep Learning Approach for Small Surface Water Area Recognition Onboard Satellite
+
 ### Segmentation - Fire, smoke & burn areas
 
 - [SatelliteVu-AWS-Disaster-Response-Hackathon](https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon) -> fire spread prediction using classical ML & deep learning
@@ -610,6 +615,8 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [Methane-detection-from-hyperspectral-imagery](https://github.com/satish1901/Methane-detection-from-hyperspectral-imagery) -> Deep Remote Sensing Methods for Methane Detection in Overhead Hyperspectral Imagery
 
+- [methane-emission-project](https://github.com/stlbnmaria/methane-emission-project) -> Classification CNNs was combined in an ensemble approach with traditional methods on tabular data
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 - [EddyNet](https://github.com/redouanelg/EddyNet) -> A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
 
 - [schisto-vegetation](https://github.com/deleo-lab/schisto-vegetation) -> Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa
@@ -953,6 +960,14 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection from Aerial Images](https://github.com/links-ads/access-solar-panels)
 
+### Segmentation - Ships & vessels
+
+- [Universal-segmentation-baseline-Kaggle-Airbus-Ship-Detection](https://github.com/OniroAI/Universal-segmentation-baseline-Kaggle-Airbus-Ship-Detection) -> Kaggle Airbus Ship Detection Challenge - bronze medal solution
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+- [Airbus-Ship-Segmentation](https://github.com/TheXirex/Airbus-Ship-Segmentation) -> unet
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+- [contrastive_SSL_ship_detection](https://github.com/alina2204/contrastive_SSL_ship_detection) -> Contrastive self supervised learning for ship detection in Sentinel 2 images
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 ### Segmentation - Other manmade
 
 - [Aarsh2001/ML_Challenge_NRSC](https://github.com/Aarsh2001/ML_Challenge_NRSC) -> Electrical Substation detection
@@ -961,16 +976,18 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [MCAN-OilSpillDetection](https://github.com/liyongqingupc/MCAN-OilSpillDetection) -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training
 
-- [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
 
 - [EG-UNet](https://github.com/tist0bsc/EG-UNet) Deep Feature Enhancement Method for Land Cover With Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining
 
+- [plastics](https://github.com/earthrise-media/plastics) -> Detecting and Monitoring Plastic Waste Aggregations in Sentinel-2 Imagery for [globalplasticwatch.org](https://globalplasticwatch.org/)
+
 - [MADOS](https://github.com/gkakogeorgiou/mados) -> Detecting Marine Pollutants and Sea Surface Features with Deep Learning in Sentinel-2 Imagery on the MADOS dataset
 
 - [SADMA](https://github.com/sheikhazhanmohammed/SADMA) -> Residual Attention UNet on MARIDA: Marine Debris Archive is a marine debris-oriented dataset on Sentinel-2 satellite images
 
+- [MAP-Mapper](https://github.com/CoDIS-Lab/MAP-Mapper) -> Marine Plastic Mapper is a tool for assessing marine macro-plastic density to identify plastic hotspots, underpinned by the MARIDA dataset.
+
 ### Panoptic segmentation
 
 - [Things and stuff or how remote sensing could benefit from panoptic segmentation](https://softwaremill.com/things-and-stuff-or-how-remote-sensing-could-benefit-from-panoptic-segmentation/)
@@ -1137,6 +1154,8 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 - [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
 
+- [SSRS](https://github.com/sstary/SSRS) -> Semantic Segmentation for Remote Sensing, multiple networks implemented
+
 #
 ## Instance segmentation
 
@@ -1874,6 +1893,10 @@ Clouds are a major issue in remote sensing images as they can obscure the underl
 
 - [cloud-detection-venus](https://github.com/pesekon2/cloud-detection-venus) -> Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types
 
+- [UnCRtainTS](https://github.com/PatrickTUM/UnCRtainTS) -> Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
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+- [U-TILISE](https://github.com/prs-eth/U-TILISE) -> A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
+
 #
 ## Change detection
 
@@ -2167,6 +2190,14 @@ Change detection is a vital component of remote sensing analysis, enabling the m
 
 - [I3PE](https://github.com/ChenHongruixuan/I3PE) -> Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange
 
+- [BDANet](https://github.com/ShaneShen/BDANet-Building-Damage-Assessment) -> Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
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+- [BAN](https://github.com/likyoo/BAN) -> A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection
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+- [ubdd](https://github.com/fzmi/ubdd) -> Learning Efficient Unsupervised Satellite Image-based Building Damage Detection, uses xView2
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+- [SGSLN](https://github.com/NJU-LHRS/offical-SGSLN) -> Exchanging Dual-Encoder–Decoder: A New Strategy for Change Detection With Semantic Guidance and Spatial Localization
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 #
 ## Time series
 
@@ -3091,6 +3122,8 @@ Mixed data learning is the process of learning from datasets that may contain an
 
 - [Sub-meter resolution canopy height map by Meta](https://research.facebook.com/blog/2023/4/every-tree-counts-large-scale-mapping-of-canopy-height-at-the-resolution-of-individual-trees/) -> Satellite Metadata combined with outputs from simple CNN to regress canopy height
 
+- [methane-emission-project](https://github.com/stlbnmaria/methane-emission-project) -> Classification CNNs was combined in an ensemble approach with traditional methods on tabular data
+
 #
 ## 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.