Add OSM

README.md

@@ -213,6 +213,7 @@ Several different techniques can be used to count the number of objects in an im
 * [Classifying Ships in Satellite Imagery with Neural Networks](https://towardsdatascience.com/classifying-ships-in-satellite-imagery-with-neural-networks-944024879651) -> applied to the Kaggle Ships in Satellite Imagery dataset
 * [Ship Detection in Satellite Images using Mask R-CNN](https://spell.ml/blog/ship-detection-in-satellite-images-using-mask-r-cnn-XyMA4xEAACUAb9G1) blog post by spell.ml
 * [Mask R-CNN for Ship Detection & Segmentation](https://medium.com/@gabogarza/mask-r-cnn-for-ship-detection-segmentation-a1108b5a083) blog post with [repo](https://github.com/gabrielgarza/Mask_RCNN)
+* [contrastive_SSL_ship_detection](https://github.com/alina2204/contrastive_SSL_ship_detection) -> Contrastive self supervised learning for ship detection in Sentinel 2 images
 
 #### Object detection - vehicles & trains
 * [Truck Detection with Sentinel-2 during COVID-19 crisis](https://github.com/hfisser/Truck_Detection_Sentinel2_COVID19) -> moving objects in Sentinel-2 data causes a specific reflectance relationship in the RGB, which looks like a rainbow, and serves as a marker for trucks. Improve accuracy by only analysing roads. Not using object detection but relevant
@@ -356,6 +357,8 @@ Translate images e.g. from SAR to RGB.
 * [satellite-to-satellite-translation](https://github.com/anonymous-ai-for-earth/satellite-to-satellite-translation) -> VAE-GAN architecture for unsupervised image-to-image translation with shared spectral reconstruction loss. Model is trained on GOES-16/17 and Himawari-8 L1B data
 * [Pytorch implementation of UNet for converting aerial satellite images into google maps kinda images](https://github.com/greed2411/unet_pytorch)
 * [Seamless-Satellite-image-Synthesis](https://github.com/Misaliet/Seamless-Satellite-image-Synthesis) -> generate abitrarily large RGB images from a map
+* [How to Develop a Pix2Pix GAN for Image-to-Image Translation](https://machinelearningmastery.com/how-to-develop-a-pix2pix-gan-for-image-to-image-translation/) -> article on machinelearningmastery.com
+* [Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework](https://github.com/anh-nn01/Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework)
 
 ## GANS
 * [Anomaly Detection on Mars using a GAN](https://omdena.com/projects/anomaly-detection-mars/)
@@ -395,6 +398,7 @@ The terms self-supervised, semi-supervised, un-supervised, contrastive learning
 * [Tensorflow similarity](https://github.com/tensorflow/similarity) -> offers state-of-the-art algorithms for metric learning and all the necessary components to research, train, evaluate, and serve similarity-based models
 * [deepsentinel](https://github.com/Lkruitwagen/deepsentinel) -> a sentinel-1 and -2 self-supervised sensor fusion model for general purpose semantic embedding
 * [Semi-supervised learning in satellite image classification](https://medium.com/sentinel-hub/semi-supervised-learning-in-satellite-image-classification-e0874a76fc61) -> experimenting with MixMatch and the EuroSAT data set
+* [contrastive_SSL_ship_detection](https://github.com/alina2204/contrastive_SSL_ship_detection) -> Contrastive self supervised learning for ship detection in Sentinel 2 images
 
 ## Active learning
 Supervised deep learning techniques typically require a huge number of labelled examples for form a training dataset. However labelling at scale take significant time, expertise and resources. Active learning techniques aim to reduce the total amount of annotation that needs to be performed by selecting the most useful images to label from a large pool of unlabelled examples, thus reducing the time to generate training datasets. These processes may be referred to as [Human-in-the-Loop Machine Learning](https://medium.com/pytorch/https-medium-com-robert-munro-active-learning-with-pytorch-2f3ee8ebec)
@@ -444,6 +448,7 @@ Image fusion of low res multispectral with high res pan band.
 * [Convolutional autoencoder for image denoising](https://keras.io/examples/vision/autoencoder/) -> keras guide
 * [piq](https://github.com/photosynthesis-team/piq) -> a collection of measures and metrics for image quality assessment
 * [FFA-Net](https://github.com/zhilin007/FFA-Net) -> Feature Fusion Attention Network for Single Image Dehazing
+* [DeepCalib](https://github.com/alexvbogdan/DeepCalib) -> A Deep Learning Approach for Automatic Intrinsic Calibration of Wide Field-of-View Cameras
 
 ## Image registration
 Image registration is the process of transforming different sets of data into one coordinate system. Typical use is overlapping images taken at different times or with different cameras.
@@ -487,6 +492,7 @@ Measure surface contours.
 * [ResDepth](https://github.com/stuckerc/ResDepth) -> A Deep Prior For 3D Reconstruction From High-resolution Satellite Images
 * [overhead-geopose-challenge](https://www.drivendata.org/competitions/78/overhead-geopose-challenge/) -> competition to build computer vision algorithms that can effectively model the height and pose of ground objects for monocular satellite images taken from oblique angles. Blog post [MEET THE WINNERS OF THE OVERHEAD GEOPOSE CHALLENGE](https://www.drivendata.co/blog/overhead-geopose-challenge-winners/)
 * [cars](https://github.com/CNES/cars) -> a dedicated and open source 3D tool to produce Digital Surface Models from satellite imaging by photogrammetry. This Multiview stereo pipeline is intended for massive DSM production with a robust and performant design
+* [ImageToDEM](https://github.com/Panagiotou/ImageToDEM) -> Generating Elevation Surface from a Single RGB Remotely Sensed Image Using a U-Net for generator and a PatchGAN for the discriminator
 
 ## Thermal Infrared
 * [The World Needs (a lot) More Thermal Infrared Data from Space](https://towardsdatascience.com/the-world-needs-a-lot-more-thermal-infrared-data-from-space-dbbba389be8a)
@@ -1097,6 +1103,7 @@ Arguably the most significant paid software for working with maps and geographic
 * [nvtop](https://github.com/Syllo/nvtop) -> NVIDIA GPUs htop like monitoring tool
 
 ## QGIS
+A popular open source alternative to ArcGIS, desktop appication written in python and extended with plugins
 * [QGIS](https://qgis.org/en/site/)
 * Create, edit, visualise, analyse and publish geospatial information. Open source alternative to ArcGIS.
 * [Python scripting](https://docs.qgis.org/testing/en/docs/pyqgis_developer_cookbook/intro.html#scripting-in-the-python-console)
@@ -1105,9 +1112,10 @@ Arguably the most significant paid software for working with maps and geographic
 * [DeepLearningTools plugin](https://plugins.qgis.org/plugins/DeepLearningTools/) -> aid training Deep Learning Models
 * [Mapflow.ai plugin](https://www.gislounge.com/run-ai-mapping-in-qgis-over-high-resolution-satellite-imagery/) -> various models to extract building footprints etc from Maxar imagery
 * [dzetsaka plugin](https://github.com/nkarasiak/dzetsaka) -> classify different kind of vegetation
+* [Coregistration-Qgis-processing](https://github.com/SMByC/Coregistration-Qgis-processing) -> Qgis processing plugin for image co-registration; projection and pixel alignment based on a target image, uses Arosics
 
 ## GDAL & Rasterio
-* So improtant this pair gets their own section. GDAL is THE command line tool for reading and writing raster and vector geospatial data formats. If you are using python you will probably want to use Rasterio which provides a pythonic wrapper for GDAL
+So improtant this pair gets their own section. GDAL is THE command line tool for reading and writing raster and vector geospatial data formats. If you are using python you will probably want to use Rasterio which provides a pythonic wrapper for GDAL
 * [GDAL](https://gdal.org) and [on twitter](https://twitter.com/gdaltips)
 * GDAL is a dependency of Rasterio and can be difficult to build and install. I recommend using conda, brew (on OSX) or docker in these situations
 * GDAL docker quickstart: `docker pull osgeo/gdal` then `docker run --rm -v $(pwd):/data/ osgeo/gdal gdalinfo /data/cog.tiff`
@@ -1172,7 +1180,6 @@ Arguably the most significant paid software for working with maps and geographic
 * [sewar](https://github.com/andrewekhalel/sewar) -> All image quality metrics you need in one package
 * [fiftyone](https://github.com/voxel51/fiftyone) -> open-source tool for building high quality datasets and computer vision models. Visualise labels, evaluate model predictions, explore scenarios of interest, identify failure modes, find annotation mistakes, and much more!
 * [GeoTagged_ImageChip](https://github.com/Hejarshahabi/GeoTagged_ImageChip) -> A simple script to create geo tagged image chips from high resolution RS iamges for training deep learning models such as Unet.
-* [Label Maker](https://github.com/developmentseed/label-maker) -> downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an `.npz` file for use in machine learning training. This should be used instead of the deprecated [skynet-data](https://github.com/developmentseed/skynet-data)
 * [Satellite imagery label tool](https://github.com/calebrob6/labeling-tool) -> provides an easy way to collect a random sample of labels over a given scene of satellite imagery
 * [DeepSatData](https://github.com/michaeltrs/DeepSatData) -> Automatically create machine learning datasets from satellite images
 * [image-reconstructor-patches](https://github.com/marijavella/image-reconstructor-patches) -> Reconstruct Image from Patches with a Variable Stride
@@ -1190,6 +1197,8 @@ Arguably the most significant paid software for working with maps and geographic
 * [Export thumbnails from Earth Engine](https://gorelick.medium.com/fast-er-downloads-a2abd512aa26)
 * [datumaro](https://github.com/openvinotoolkit/datumaro) -> Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets
 * [patchify](https://github.com/dovahcrow/patchify.py) -> A library that helps you split image into small, overlappable patches, and merge patches into original image
+* [ohsome2label](https://github.com/GIScience/ohsome2label) -> Historical OpenStreetMap (OSM) Objects to Machine Learning Training Samples
+* [Label Maker](https://github.com/developmentseed/label-maker) -> downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an `.npz` file for use in machine learning training. This should be used instead of the deprecated [skynet-data](https://github.com/developmentseed/skynet-data)
 
 ## Image augmentation packages
 Image augmentation is a technique used to expand a training dataset in order to improve ability of the model to generalise
@@ -1233,7 +1242,15 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [sentinelsat](https://github.com/sentinelsat/sentinelsat) -> Search and download Copernicus Sentinel satellite images
 * [landsatxplore](https://github.com/yannforget/landsatxplore) -> Search and download Landsat scenes from EarthExplorer
 * [OpenSarToolkit](https://github.com/ESA-PhiLab/OpenSarToolkit) -> High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the python language
+
+## OpenStreetMap
+[OpenStreetMap](https://www.openstreetmap.org/) (OSM) is a map of the world, created by people like you and free to use under an open license. Quite a few publications use OSM data for annotations & ground truth. Note that the data is created by volunteers and the quality can be variable
 * [osmnx](https://github.com/gboeing/osmnx) -> Retrieve, model, analyze, and visualize data from OpenStreetMap
+* [ohsome2label](https://github.com/GIScience/ohsome2label) -> Historical OpenStreetMap Objects to Machine Learning Training Samples
+* [Label Maker](https://github.com/developmentseed/label-maker) -> downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an `.npz` file for use in machine learning training. This should be used instead of the deprecated [skynet-data](https://github.com/developmentseed/skynet-data)
+* [prettymaps](https://github.com/marceloprates/prettymaps) -> A small set of Python functions to draw pretty maps from OpenStreetMap data
+* [Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps](https://arxiv.org/abs/1705.06057) -> fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks, arxiv paper
+* [Identifying Buildings in Satellite Images with Machine Learning and Quilt](https://github.com/jyamaoka/LandUse) -> NDVI & edge detection via gaussian blur as features, fed to TPOT for training with labels from OpenStreetMap, modelled as a two class problem, “Buildings” and “Nature”
 
 ## Graphing and visualisation
 * [hvplot](https://hvplot.holoviz.org/) -> A high-level plotting API for the PyData ecosystem built on HoloViews. Allows overlaying data on map tiles, see [Exploring USGS Terrain Data in COG format using hvPlot](https://discourse.holoviz.org/t/exploring-usgs-terrain-data-in-cog-format-using-hvplot/1727)