Update README.md

README.md

@@ -1065,7 +1065,7 @@ Super-resolution attempts to enhance the resolution of an imaging system, and ca
 * [sr4rs](https://github.com/remicres/sr4rs) -> Super resolution for remote sensing, with pre-trained model for Sentinel-2, SRGAN-inspired
 * [Restoring old aerial images with Deep Learning](https://towardsdatascience.com/restoring-old-aerial-images-with-deep-learning-60f0cfd2658) -> Medium article on Super Resolution with Perceptual Loss function and real images as input
 * [RFSR_TGRS](https://github.com/wxywhu/RFSR_TGRS) -> code for the paper Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and Spatial-Spectral Consistency Regularization
-* [SEN2VENµS](https://zenodo.org/record/6514159#.YoRxM5PMK3I) -> a dataset for the training of Sentinel-2 super-resolution algorithms
+* [SEN2VENµS](https://zenodo.org/record/6514159#.YoRxM5PMK3I) -> a dataset for the training of Sentinel-2 super-resolution algorithms. With [paper](https://www.mdpi.com/2306-5729/7/7/96)
 * [TransENet](https://github.com/Shaosifan/TransENet) -> code for 2021 paper: Transformer-based Multi-Stage Enhancement for Remote Sensing Image Super-Resolution
 * [SG-FBGAN](https://github.com/hanlinwu/SG-FBGAN) -> code for 2020 [paper](https://ieeexplore.ieee.org/document/9301233): Remote Sensing Image Super-Resolution via Saliency-Guided Feedback GANs
 * [finetune_ESRGAN](https://github.com/johnjaniczek/finetune_ESRGAN) -> finetune the ESRGAN super resolution generator for remote sensing images and video
@@ -1256,6 +1256,7 @@ This is a class of techniques which attempt to make predictions for classes with
 * [P-CNN](https://github.com/Ybowei/P-CNN) -> code for 2021 [paper](https://ieeexplore.ieee.org/document/9435769): Prototype-CNN for Few-Shot Object Detection in Remote Sensing Images
 * [CIR-FSD-2022](https://github.com/Li-ZK/CIR-FSD-2022) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/14/3255): Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images
 * [IEEE_TNNLS_Gia-CFSL](https://github.com/YuxiangZhang-BIT/IEEE_TNNLS_Gia-CFSL) -> code for 2022 [paper](https://ieeexplore.ieee.org/document/9812472): Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification
+* [TIP_2022_CMFSL](https://github.com/B-Xi/TIP_2022_CMFSL) -> code for 2022 [paper](https://ieeexplore.ieee.org/document/9841445): Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification
 
 ## Self-supervised, unsupervised & contrastive learning
 These techniques use unlabelled datasets. [Yann LeCun](https://braindump.jethro.dev/posts/lecun_cake_analogy/) has described self/unsupervised learning as the 'base of the cake': *If we think of our brain as a cake, then the cake base is unsupervised learning. The machine predicts any part of its input for any observed part, all without the use of labelled data. Supervised learning forms the icing on the cake, and reinforcement learning is the cherry on top.*
@@ -1628,6 +1629,9 @@ This section contains a short list of datasets relevant to deep learning, partic
 * [Space2Ground](https://github.com/Agri-Hub/Space2Ground) -> dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.
 * [sentinel2tools](https://github.com/QuantuMobileSoftware/sentinel2tools) -> downloading & basic processing of Sentinel 2 imagesry. Read [Sentinel2tools: simple lib for downloading Sentinel-2 satellite images](https://medium.com/geekculture/sentinel2tools-simple-lib-for-downloading-sentinel-2-satellite-images-f8a6be3ee894)
 * [open-sentinel-map](https://github.com/VisionSystemsInc/open-sentinel-map) -> The OpenSentinelMap dataset contains Sentinel-2 imagery and per-pixel semantic label masks derived from OpenStreetMap
+* [MSCDUnet](https://github.com/Lihy256/MSCDUnet) -> change detection datasets containing VHR, multispectral (Sentinel-2) and SAR (Sentinel-1)
+* [OMBRIA](https://github.com/geodrak/OMBRIA) -> Sentinel-1 & 2 dataset for adressing the flood mapping problem
+* [Canadian-cropland-dataset](https://github.com/bioinfoUQAM/Canadian-cropland-dataset) -> a novel patch-based dataset compiled using optical satellite images of Canadian agricultural croplands retrieved from Sentinel-2
 
 ## Landsat
 * Long running US program -> see [Wikipedia](https://en.wikipedia.org/wiki/Landsat_program)
@@ -1859,6 +1863,7 @@ Since there is a whole community around GEE I will not reproduce it here but lis
 ### DEM (digital elevation maps)
 * Shuttle Radar Topography Mission, search online at usgs.gov
 * Copernicus Digital Elevation Model (DEM) on S3, represents the surface of the Earth including buildings, infrastructure and vegetation. Data is provided as Cloud Optimized GeoTIFFs. [link](https://registry.opendata.aws/copernicus-dem/)
+* [Awesome-DEM](https://github.com/DahnJ/Awesome-DEM)
 
 ## UAV & Drone datasets
 * Many on https://www.visualdata.io
@@ -1916,10 +1921,8 @@ Since there is a whole community around GEE I will not reproduce it here but lis
 * [Haiming-Z/MtS-WH-reference-map](https://github.com/Haiming-Z/MtS-WH-reference-map) -> a reference map for change detection based on MtS-WH
 * [MtS-WH-Dataset](https://github.com/rulixiang/MtS-WH-Dataset) -> Multi-temporal Scene WuHan (MtS-WH) Dataset
 * [Multi-modality-image-matching](https://github.com/StaRainJ/Multi-modality-image-matching-database-metrics-methods) -> image matching dataset including several remote sensing modalities
-* [Canadian-cropland-dataset](https://github.com/bioinfoUQAM/Canadian-cropland-dataset) -> a novel patch-based dataset compiled using optical satellite images of Canadian agricultural croplands retrieved from Sentinel-2
 * [RID](https://github.com/TUMFTM/RID) -> Roof Information Dataset for CV-Based Photovoltaic Potential Assessment. With [paper](https://www.mdpi.com/2072-4292/14/10/2299)
 * [APKLOT](https://github.com/langheran/APKLOT) -> A dataset for aerial parking block segmentation
-* [MSCDUnet](https://github.com/Lihy256/MSCDUnet) -> change detection datasets containing VHR, multispectral (Sentinel-2) and SAR (Sentinel-1) 
 
 ## Kaggle
 Kaggle hosts over > 200 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets).
@@ -2348,7 +2351,7 @@ Also check the section **Image handling, manipulation & dataset creation**
 * [labelImg](https://github.com/tzutalin/labelImg) is the classic desktop tool, limited to bounding boxes for object detection. Also checkout [roLabelImg](https://github.com/cgvict/roLabelImg) which supports ROTATED rectangle regions, as often occurs in aerial imagery. [labelImg_OBB](https://github.com/heshameraqi/labelImg_OBB) is another fork supporting orinted bounding boxes (OBB)
 * [Labelme](https://github.com/wkentaro/labelme) is a very popular & simple dektop app for polygonal annotation suitable for object detection and semantic segmentation. Note it outputs annotations in a custom LabelMe JSON format which you will need to convert, e.g. using [labelme2coco](https://github.com/fcakyon/labelme2coco). Read [Labelme Image Annotation for Geotiffs](https://medium.com/@wvsharber/labelme-image-annotation-for-geotiffs-b460ba83804f)
 * [Label Studio](https://labelstud.io/) is a multi-type data labeling and annotation tool with standardized output format, syncing to buckets, and supports importing pre-annotations (create with a model). Checkout [label-studio-converter](https://github.com/heartexlabs/label-studio-converter) for converting Label Studio annotations into common dataset formats
-* [CVAT](https://github.com/openvinotoolkit/cvat) suports object detection, segmentation and classification via a local web app. There is an [open issue](https://github.com/openvinotoolkit/cvat/issues/531) to support large TIFF files. [This article on Roboflow](https://blog.roboflow.com/cvat/) gives a good intro to CVAT. Checkout [CVAT images validator](https://github.com/developmentseed/cvat-images-validator)
+* [CVAT](https://github.com/cvat-ai/cvat) suports object detection, segmentation and classification via a local web app. [This article on Roboflow](https://blog.roboflow.com/cvat/) gives a good intro to CVAT. Checkout [CVAT images validator](https://github.com/developmentseed/cvat-images-validator)
 * [VoTT](https://github.com/Microsoft/VoTT) -> an electron app for building end to end Object Detection Models from Images and Videos, by Microsoft
 * Create your own annotation tool using [Bokeh Holoviews](https://examples.pyviz.org/ml_annotators/ml_annotators.html#ml-annotators-gallery-ml-annotators), [tkinter](https://github.com/matpalm/bnn#labelling), or see these dash examples for [object detection](https://github.com/plotly/dash-sample-apps/tree/main/apps/dash-image-annotation) and [segmentation](https://github.com/plotly/dash-sample-apps/tree/main/apps/dash-image-segmentation)
 * [Deeplabel](https://github.com/jveitchmichaelis/deeplabel) is a cross-platform tool for annotating images with labelled bounding boxes. Deeplabel also supports running inference using state-of-the-art object detection models like Faster-RCNN and YOLOv4. With support out-of-the-box for CUDA, you can quickly label an entire dataset using an existing model.
@@ -2469,6 +2472,7 @@ Scripts and command line applications
 * [AGBench](https://github.com/gyrrei/AGBench) -> a Python library that benchmarks satellite-based aboveground biomass or carbon estimate maps
 * [mbtiles-s3-server](https://github.com/uktrade/mbtiles-s3-server) -> Python server to on-the-fly extract and serve vector tiles from an mbtiles file on S3
 * [matico](https://github.com/Matico-Platform/matico) -> a set of tools and services that allow users to manage geospatial datasets, build APIs that use those datasets and full geospatial applications with little to no code
+* [gmtsar](https://github.com/mobigroup/gmtsar) -> easy and fast satellite interferometry (InSAR) processing
 
 ## Low level numerical & data formats
 * [xarray](http://xarray.pydata.org/en/stable/) -> N-D labeled arrays and datasets. Read [Handling multi-temporal satellite images with Xarray](https://medium.com/@bonnefond.virginie/handling-multi-temporal-satellite-images-with-xarray-30d142d3391). Checkout [xarray_leaflet](https://github.com/davidbrochart/xarray_leaflet) for tiled map plotting and [sklearn-xarray](https://github.com/phausamann/sklearn-xarray) for metadata-aware machine learning. Publish Xarray Datasets via a REST API uisng [xpublish](https://github.com/xarray-contrib/xpublish)
@@ -3048,6 +3052,7 @@ For a full list of companies, on and off Github, checkout [awesome-geospatial-co
 * [Terrawatch Space](https://anchor.fm/terrawatch-space)
 * [Geomob](https://thegeomob.com/)
 * [Project Geospatial](https://podcasts.apple.com/us/podcast/project-geospatial/id1486384184)
+* [Zenml episode: Satellite Vision with Robin Cole](https://podcast.zenml.io/satellite-vision-robin-cole)
 
 # Newsletters
 * [Radiant Earth ML4EO market news](https://www.radiant.earth/category/ml4eo-market-news/)