diff --git a/README.md b/README.md
index ea0e6d40..594661cf 100644
--- a/README.md
+++ b/README.md
@@ -61,6 +61,9 @@ or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial
 * [landcover_classification](https://github.com/reidfalconer/landcover_classification) -> using fast.ai on EuroSAT
 * [IGARSS2020_BWMS](https://github.com/jiankang1991/IGARSS2020_BWMS) -> Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification with a novel CNN architecture for the feature embedding of high-dimensional RS images
 * [image.classification.on.EuroSAT](https://github.com/canturan10/image.classification.on.EuroSAT) -> solution in pure pytorch
+* [hurricane_damage](https://github.com/allankapoor/hurricane_damage) -> Post-hurricane structure damage assessment based on aerial imagery with CNN
+* [openai-drivendata-challenge](https://github.com/buildwithcycy/openai-drivendata-challenge) -> Using deep learning to classify the building material of rooftops (aerial imagery from South America)
+* [is-it-abandoned](https://github.com/zach-brown-18/is-it-abandoned) -> Can we tell if a house is abandoned based on aerial LIDAR imagery?
 
 ## Segmentation
 Segmentation will assign a class label to each **pixel** in an image. Segmentation is typically grouped into semantic, instance or panoptic segmentation. In semantic segmentation objects of the same class are assigned the same label, whilst in instance segmentation each object is assigned a unique label. Panoptic segmentation combines instance and semantic predictions. Read this [beginner’s guide to segmentation](https://medium.com/gsi-technology/a-beginners-guide-to-segmentation-in-satellite-images-9c00d2028d52). Single class models are often trained for road or building segmentation, with multi class for land use/crop type classification. Image annotation can take longer than for object detection since every pixel must be annotated. **Note** that many articles which refer to 'hyperspectral land classification' are actually describing semantic segmentation.
@@ -85,6 +88,8 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [Implementing Transfer Learning from RGB to Multi-channel Imagery](https://towardsdatascience.com/implementing-transfer-learning-from-rgb-to-multi-channel-imagery-f87924679166) -> Semantic Segmentation using ResNet50 Backbone plus Pyramid Pooling
 * [clusternet_segmentation](https://github.com/zhygallo/clusternet_segmentation) -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network
 * [DCA](https://github.com/Luffy03/DCA) -> Code for TGRS 2022 paper "Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation"
+* [weakly_supervised](https://github.com/LobellLab/weakly_supervised) -> code for the paper Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. Demonstrates that segmentation can be performed using small datasets comprised of pixel or image labels
+* [Collection of different Unet Variant](https://github.com/ashishpatel26/satellite-Image-Semantic-Segmentation-Unet-Tensorflow-keras) -> demonstrates VggUnet, ResUnet, DenseUnet, Unet. AttUnet, MobileNetUnet, NestedUNet, R2AttUNet, R2UNet, SEUnet, scSEUnet, Unet_Xception_ResNetBlock, in keras
 
 ### Semantic segmentation - multiclass classification
 * [Land Cover Classification with U-Net](https://baratam-tarunkumar.medium.com/land-cover-classification-with-u-net-aa618ea64a1b) -> Satellite Image Multi-Class Semantic Segmentation Task with PyTorch Implementation of U-Net
@@ -103,6 +108,7 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [solar-panel-locator](https://github.com/TorrBorr/solar-panel-locator) -> the number of solar panel pixels was only ~0.2% of the total pixels in the dataset, so solar panel data was upsampled to account for the class imbalance
 * [projects-solar-panel-detection](https://github.com/top-on/projects-solar-panel-detection) -> List of project to detect solar panels from aerial/satellite images
 * [Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation](https://affine.medium.com/detectron2-fpn-pointrend-model-for-amazing-satellite-image-segmentation-183456063e15) -> 15% increase in accuracy when compared to the U-Net model
+* [Fusing multiple segmentation models based on different datasets into a single edge-deployable model](https://github.com/markusmeingast/Satellite-Classifier) -> roof, car & road segmentation
 
 ### Semantic segmentation - buildings & rooftops
 * [Semantic Segmentation on Aerial Images using fastai](https://medium.com/swlh/semantic-segmentation-on-aerial-images-using-fastai-a2696e4db127) uses U-Net on the Inria Aerial Image Labeling Dataset of urban settlements in Europe and the United States, and is labelled as a building and not building classes (no repo)
@@ -136,6 +142,11 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [Mask RCNN for Spacenet Off Nadir Building Detection](https://github.com/ashnair1/Mask-RCNN-for-Off-Nadir-Building-Detection)
 * [GRSL_BFE_MA](https://github.com/jiankang1991/GRSL_BFE_MA) -> Deep Learning-based Building Footprint Extraction with Missing Annotations using a novel loss function
 * [FER-CNN](https://github.com/runnergirl13/FER-CNN) -> Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks, with [paper](https://www.mdpi.com/2072-4292/12/14/2240/htm)
+* [UNET-Image-Segmentation-Satellite-Picture](https://github.com/rwie1and/UNET-Image-Segmentation-Satellite-Pictures) -> Unet to predict roof tops on Crowed AI Mapping dataset, uses keras
+* [Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET](https://github.com/ManishSahu53/Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET) -> applied to geo-referenced images which are very large size > 10k x 10k pixels
+* [building-footprint-segmentation](https://github.com/fuzailpalnak/building-footprint-segmentation) -> pip installable library to train building footprint segmentation on satellite and aerial imagery, applied to Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset
+* [SemSegBuildings](https://github.com/SharpestProjects/SemSegBuildings) -> Project using fast.ai framework for semantic segmentation on Inria building segmentation dataset
+* [FCNN-example](https://github.com/emredog/FCNN-example) -> overfit to a given single image to detect houses
 
 ### Semantic segmentation - solar panels
 * [DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States](https://www.cell.com/joule/fulltext/S2542-4351(18)30570-1) -> with [website](http://web.stanford.edu/group/deepsolar/home), [repo](https://github.com/wangzhecheng/DeepSolar) and [dataset on kaggle](https://www.kaggle.com/tunguz/deep-solar-dataset), actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but [tf2/kers](https://github.com/aidan-fitz/deepsolar-v2) and a [pytorch implementation](https://github.com/wangzhecheng/deepsolar_pytorch) are available
@@ -152,6 +163,7 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [remote-sensing-solar-pv](https://github.com/Lkruitwagen/remote-sensing-solar-pv) -> A repository for sharing progress on the automated detection of solar PV arrays in sentinel-2 remote sensing imagery
 * [solar-panel-segmentation)](https://github.com/gabrieltseng/solar-panel-segmentation) -> Finding solar panels using USGS satellite imagery
 * [solar_seg](https://github.com/tcapelle/solar_seg) -> Solar segmentation of PV modules (sub elements of panels) using drone images and fast.ai
+* [solar_plant_detection](https://github.com/Amirmoradi94/solar_plant_detection) -> boundary extraction of Photovoltaic (PV) plants using Mask RCNN and Amir dataset
 
 ### Semantic segmentation - roads
 * [Semantic segmentation of roads and highways using Sentinel-2 imagery (10m) super-resolved using the SENX4 model up to x4 the initial spatial resolution (2.5m)](https://tracasa.es/innovative-stories/sen2roadlasviastambiensevendesdesentinel-2/) (results, no repo)
@@ -167,6 +179,7 @@ For multi/hyper-spectral imagery, classical techniques may be used (e.g. k-means
 * [Road-Network-Extraction using classical Image processing](https://github.com/abhaykes1/Road-Network-Extraction) -> blur & canny edge detection
 * [SPIN_RoadMapper](https://github.com/wgcban/SPIN_RoadMapper) -> Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
 * [road_extraction_remote_sensing](https://github.com/jiankang1991/road_extraction_remote_sensing) -> pytorch implementation, CVPR2018 DeepGlobe Challenge submission
+* [RoadDetections dataset by Microsoft](https://github.com/microsoft/RoadDetections)
 
 ### Semantic segmentation - vegitation & crop boundaries
 * [Сrор field boundary detection: approaches overview and main challenges](https://soilmate.medium.com/%D1%81r%D0%BE%D1%80-field-boundary-detection-approaches-overview-and-main-challenges-53736725cb06) - review article, no code
@@ -272,6 +285,8 @@ Several different techniques can be used to count the number of objects in an im
 * [CorrNet](https://github.com/MathLee/CorrNet) -> Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
 * [Reading list for deep learning based Salient Object Detection in Optical Remote Sensing Images](https://github.com/MathLee/ORSI-SOD_Summary)
 * [Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker](https://arxiv.org/abs/1804.09323) arxiv paper
+* [Object Detection On Aerial Imagery Using RetinaNet](https://towardsdatascience.com/object-detection-on-aerial-imagery-using-retinanet-626130ba2203) -> ESRI Data Science Challenge 2019 3rd place solution
+* [Clustered-Object-Detection-in-Aerial-Image](https://github.com/fyangneil/Clustered-Object-Detection-in-Aerial-Image)
 
 #### Object detection enhanced by super resolution
 * [Super-Resolution and Object Detection](https://medium.com/the-downlinq/super-resolution-and-object-detection-a-love-story-part-4-8ad971eef81e) -> Super-resolution is a relatively inexpensive enhancement that can improve object detection performance
@@ -296,6 +311,8 @@ Several different techniques can be used to count the number of objects in an im
 * [Rotated-RetinaNet](https://github.com/ming71/Rotated-RetinaNet) -> implemented in pytorch, it supports the following datasets: DOTA, HRSC2016, ICDAR2013, ICDAR2015, UCAS-AOD, NWPU VHR-10, VOC2007
 * [OBBDet_Swin](https://github.com/ming71/OBBDet_Swin) -> The sixth place winning solution in 2021 Gaofen Challenge
 * [CG-Net](https://github.com/WeiZongqi/CG-Net) -> Learning Calibrated-Guidance for Object Detection in Aerial Images
+* [OrientedRepPoints_DOTA](https://github.com/hukaixuan19970627/OrientedRepPoints_DOTA) -> Oriented RepPoints + Swin Transformer/ReResNet
+* [yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb) -> yolov5 + Oriented Object Detection
 
 #### Object detection - buildings, rooftops & solar panels
 * [Machine Learning For Rooftop Detection and Solar Panel Installment](https://omdena.com/blog/machine-learning-rooftops/) discusses tiling large images and generating annotations from OSM data. Features of the roofs were calculated using a combination of contour detection and classification. [Follow up article using semantic segmentation](https://omdena.com/blog/rooftops-classification/)
@@ -327,9 +344,10 @@ Several different techniques can be used to count the number of objects in an im
 * [contrastive_SSL_ship_detection](https://github.com/alina2204/contrastive_SSL_ship_detection) -> Contrastive self supervised learning for ship detection in Sentinel 2 images
 * [Boat detection with multi-region-growing method in satellite images](https://medium.com/@ipmach/boat-detection-with-multi-region-growing-method-in-satellite-images-3339a6c29a8c)
 
-#### Object detection - vehicles & trains
+#### Object detection - cars, 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
 * [cowc_car_counting](https://github.com/motokimura/cowc_car_counting) -> car counting on the [Cars Overhead With Context (COWC) dataset](https://gdo152.llnl.gov/cowc/). Not sctictly object detection but a CNN to predict the car count in a tile
+* [CarCounting](https://github.com/JacksonPeoples/CarCounting) -> using Yolov3 & COWC dataset
 * [Traffic density estimation as a regression problem instead of object detection](https://omdena.com/blog/ai-road-safety/) -> inspired by [this paper](https://ieeexplore.ieee.org/document/8916990)
 * [Applying Computer Vision to Railcar Detection](https://orbitalinsight.com/blog/apping-computer-vision-to-railcar-detection) -> useful insights into counting railcars (i.e. train carriages) using Mask-RCNN with rotated bounding boxes output
 * [Leveraging Deep Learning for Vehicle Detection And Classification](https://orbitalinsight.com/blog/leveraging-deep-learning-for-vehicle-detection-and-classification)
@@ -343,6 +361,9 @@ Several different techniques can be used to count the number of objects in an im
 * [detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5](https://medium.com/artificialis/detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5-5f3d464b75ad)
 * [Using Detectron2 to segment aircraft from satellite imagery](https://share.buitrongan.com/using-detectron2-to-segments-aircraft-from-satellite-images-5a8ac1a0d35e) -> pytorch and Rare Planes
 
+#### Object detection - infrastructure & utilities
+* [wind-turbine-detector](https://github.com/lbborkowski/wind-turbine-detector) -> Wind Turbine Object Detection from Aerial Imagery Using TensorFlow Object Detection API
+
 #### Object detection - animals
 * [cownter_strike](https://github.com/IssamLaradji/cownter_strike) -> counting cows, located with point-annotations, two models: CSRNet (a density-based method) & LCFCN (a detection-based method)
 * [elephant_detection](https://github.com/akharina/elephant_detection) -> Using Keras-Retinanet to detect elephants from aerial images
@@ -444,6 +465,9 @@ Monitor water levels, coast lines, size of urban areas, wildfire damage, crop ch
 * [siamese-change-detection](https://github.com/mvkolos/siamese-change-detection) -> Targeted synthesis of multi-temporal remote sensing images for change detection using siamese neural networks
 * [Bi-SRNet](https://github.com/ggsDing/Bi-SRNet) -> Pytorch codes of 'Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
 * [RaVAEn](https://github.com/spaceml-org/RaVAEn) -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
+* [SiROC](https://github.com/lukaskondmann/SiROC) -> Implementation of the [paper](https://ieeexplore.ieee.org/document/9627707) Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. Applied to Sentinel-2 and high-resolution Planetscope imagery on four datasets
+* [DSMSCN](https://github.com/I-Hope-Peace/DSMSCN) -> Tensorflow implementation for Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Neural Networks
+* [RaVAEn](https://github.com/spaceml-org/RaVAEn) -> a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.  It flags changed areas to prioritise for downlink, shortening the response time.
 
 ## Wealth and economic activity
 The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys
@@ -477,6 +501,7 @@ The goal is to predict economic activity from satellite imagery rather than cond
 * [Hurricane Damage Detection](https://github.com/Ryan-Awad/Hurricane-Damage-Detection) -> A convolutional neural network model used to detect hurricane damage in satellite images
 * [Machine Learning-based Damage Assessment for Disaster Relief on Google AI blog](https://ai.googleblog.com/2020/06/machine-learning-based-damage.html) -> uses object detection to locate buildings, then a classifier to determine if a building is damaged. Challenge of generalising due to small dataset
 * [RaVAEn](https://github.com/spaceml-org/RaVAEn) -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
+* [hurricane_damage](https://github.com/allankapoor/hurricane_damage) -> Post-hurricane structure damage assessment based on aerial imagery with CNN
 
 ## Super-resolution
 Super-resolution attempts to enhance the resolution of an imaging system, and can be applied as a pre-processing step to improve the detection of small objects. For an introduction to this topic [read this excellent article](https://bleedai.com/super-resolution-going-from-3x-to-8x-resolution-in-opencv/). Note that super resolution techniques are generally grouped into single image super resolution (SISR) **or** a multi image super resolution (MISR) which is typically applied to video frames.
@@ -570,6 +595,8 @@ Efforts to detect falsified images
 * [SauMoCo](https://github.com/jiankang1991/SauMoCo) -> codes for TGRS paper: Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast
 * [TGRS_RiDe](https://github.com/jiankang1991/TGRS_RiDe) -> Rotation Invariant Deep Embedding for RemoteSensing Images
 * [RaVAEn](https://github.com/spaceml-org/RaVAEn) -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
+* [Reverse image search using deep discrete feature extraction and locality-sensitive hashing](https://github.com/martenjostmann/deep-discrete-image-retrieval) 
+* [SNCA_CE](https://github.com/jiankang1991/SNCA_CE) -> code for the paper Deep Metric Learning based on Scalable Neighborhood Components for Remote Sensing Scene Characterization
 
 ## Few/one/zero/low 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.
@@ -577,8 +604,8 @@ This is a class of techniques which attempt to make predictions for classes with
 * [FSODM](https://github.com/lixiang-ucas/FSODM) -> Official Code for paper "Few-shot Object Detection on Remote Sensing Images" on [arxiv](https://arxiv.org/abs/2006.07826)
 * [Few-Shot Classification of Aerial Scene Images via Meta-Learning](https://www.mdpi.com/2072-4292/13/1/108/htm) -> 2020 publication, a classification model that can quickly adapt to unseen categories using only a few labeled samples
 
-## Self-supervised, unsupervised & contrastive learning
-The terms self-supervised, semi-supervised, unsupervised, contrastive learning & SSL describe techniques using un-labelled data. In general, the more classical techniques such as k-means classification or PCA are referred to as un-supervised, whilst newer techniques using CNN feature extraction or autoencoders are referred to as self-supervised. [Yann LeCun](https://braindump.jethro.dev/posts/lecun_cake_analogy/) has described self-supervised/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.*
+## Self-supervised, weakly/semi-supervised, unsupervised & contrastive learning
+The terms self-supervised, weakly/semi-supervised, unsupervised, contrastive learning & SSL describe techniques using un-labelled data. In general, the more classical techniques such as k-means classification or PCA are referred to as un-supervised, whilst newer techniques using CNN feature extraction or autoencoders are referred to as self-supervised. [Yann LeCun](https://braindump.jethro.dev/posts/lecun_cake_analogy/) has described self-supervised/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.*
 * [Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data](https://devblog.pytorchlightning.ai/seasonal-contrast-transferable-visual-representations-for-remote-sensing-73a17863ed07) -> Seasonal Contrast (SeCo) is an effective pipeline to leverage unlabeled data for in-domain pre-training of remote sensing representations. Models trained with SeCo achieve better performance than their ImageNet pre-trained counterparts and state-of-the-art self-supervised learning methods on multiple downstream tasks. [paper](https://arxiv.org/abs/2103.16607) and [repo](https://github.com/ElementAI/seasonal-contrast)
 * [Train SimSiam on Satellite Images](https://docs.lightly.ai/tutorials/package/tutorial_simsiam_esa.html) using [Lightly](https://docs.lightly.ai/index.html) to generate embeddings that can be used for data exploration and understanding
 * [Unsupervised Learning for Land Cover Classification in Satellite Imagery](https://omdena.com/blog/land-cover-classification/)
@@ -605,6 +632,8 @@ The terms self-supervised, semi-supervised, unsupervised, contrastive learning &
 * [sourcerer](https://github.com/benjaminmlucas/sourcerer) -> A Bayesian-inspired deep learning method for semi-supervised domain adaptation designed for land cover mapping from satellite image time series (SITS). [Paper](https://link.springer.com/article/10.1007/s10994-020-05942-z)
 * [MSMatch](https://github.com/gomezzz/MSMatch) -> Semi-Supervised Multispectral Scene Classification with Few Labels. Includes code to work with both the RGB and the multispectral (MS) versions of EuroSAT dataset and the UC Merced Land Use (UCM) dataset. [Paper](https://arxiv.org/abs/2103.10368)
 * [TheColorOutOfSpace](https://github.com/stevinc/TheColorOutOfSpace) -> Pytorch code for the paper "The color out of space: learning self-supervised representations for Earth Observation imagery" using the BigEarthNet dataset
+* [weakly_supervised](https://github.com/LobellLab/weakly_supervised) -> code for the paper Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. Demonstrates that segmentation can be performed using small datasets comprised of pixel or image labels
+* [wan](https://github.com/engrjavediqbal/wan) -> Weakly-Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery, with [arxiv paper](https://arxiv.org/abs/2007.02277)
 
 ## Active learning
 Supervised deep learning techniques typically require a huge number of annotated/labelled examples to provide 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)
@@ -686,6 +715,7 @@ Image registration is the process of transforming different sets of data into on
 * [MS_HLMO_registration](https://github.com/MrPingQi/MS_HLMO_registration) -> Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration, with [paper](https://arxiv.org/abs/2204.00260)
 * [CropTypeMapping](https://github.com/ellaampy/CropTypeMapping) -> Crop type mapping from optical and radar (Sentinel-1&2) time series using attention-based deep learning
 * [Multimodal-Remote-Sensing-Toolkit](https://github.com/likyoo/Multimodal-Remote-Sensing-Toolkit) -> uses Hyperspectral and LiDAR Data
+* [Aerial-Template-Matching](https://github.com/m-hamza-mughal/Aerial-Template-Matching) -> development of an algorithm for template Matching on aerial imagery applied to UAV dataset
 
 ## Terrain mapping, Lidar & DEMs
 Measure surface contours.
@@ -752,6 +782,7 @@ Measure surface contours.
 * [TGRS_DisOptNet](https://github.com/jiankang1991/TGRS_DisOptNet) -> Distilling Semantic Knowledge from Optical Images for Weather-independent Building Segmentation
 * [SAR_CD_DDNet](https://github.com/summitgao/SAR_CD_DDNet) -> PyTorch implementation of Change Detection in Synthetic Aperture Radar Images Using a Dual Domain Network
 * [SAR_CD_MS_CapsNet](https://github.com/summitgao/SAR_CD_MS_CapsNet) -> Code for the paper "Change Detection in SAR Images Based on Multiscale Capsule Network" IEEE GRSL 2021
+* [anomaly-detection-in-SAR-imagery](https://github.com/iamyadavabhishek/anomaly-detection-in-SAR-imagery) -> identify an unknown ship in docks using keras &  retinanet
 
 ## Neural nets in space
 Processing on board a satellite allows less data to be downlinked. e.g. super-resolution image might take 8 images to generate, then a single image is downlinked. Other applications include cloud detection and collision avoidance.
@@ -917,6 +948,7 @@ This section contains a short list of datasets relevant to deep learning, partic
 * https://project.inria.fr/aerialimagelabeling/contest/
 * RGB GeoTIFF at spatial resolution of 0.3 m
 * Data covering Austin, Chicago, Kitsap County, Western & Easter Tyrol, Innsbruck, San Francisco & Vienna
+* [SemSegBuildings](https://github.com/SharpestProjects/SemSegBuildings) -> Project using fast.ai framework for semantic segmentation on Inria building segmentation dataset
 
 ## AICrowd building segmentation dataset
 * Dataset release as part of the [mapping-challenge](https://www.aicrowd.com/challenges/mapping-challenge)
@@ -1089,6 +1121,7 @@ The xView2 Challenge focuses on automating the process of assessing building dam
 * [Microsoft Planetary Computer](https://innovation.microsoft.com/en-us/planetary-computer) is a Dask-Gateway enabled JupyterHub deployment focused on supporting scalable geospatial analysis, [source repo](https://github.com/microsoft/planetary-computer-hub)
 * [Ai for Earth program](https://www.microsoft.com/en-us/ai/ai-for-earth)
 * [landcover-orinoquia](https://github.com/microsoft/landcover-orinoquia) -> Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project
+* [RoadDetections dataset by Microsoft](https://github.com/microsoft/RoadDetections)
 
 ## Google Earth Engine (GEE)
 Since there is a whole community around GEE I will not reproduce it here but list very select references. Get started at https://developers.google.com/earth-engine/
@@ -1447,6 +1480,7 @@ A popular open source alternative to ArcGIS, desktop appication written in pytho
 * [Plugin for Landcover Classification](https://github.com/atishayjn/QGIS-Plugin) -> capable of implementing machine learning algorithms such as Random forest, SVM and CNN algorithms such as UNET through a simple GUI framework.
 * [pg_tileserv])(https://github.com/CrunchyData/pg_tileserv) -> A very thin PostGIS-only tile server in Go. Takes in HTTP tile requests, executes SQL, returns MVT tiles.
 * [pg_featureserv](https://github.com/CrunchyData/pg_featureserv) -> Lightweight RESTful Geospatial Feature Server for PostGIS in Go
+* [osm-instance-segmentation](https://github.com/mnboos/osm-instance-segmentation) -> QGIS plugin for finding changes in vector data from orthophotos (i.e. aerial imagery) using tensorflow
 
 ## 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
@@ -1626,6 +1660,8 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [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”
 * [Import OpenStreetMap data into Unreal Engine 4](https://github.com/ue4plugins/StreetMap)
+* [OSMDeepOD](https://github.com/geometalab/OSMDeepOD) ->  perform object detection with retinanet
+* [Match Bing Map Aerial Imagery with OpenStreetMap roads](https://github.com/whywww/Aerial-Imagery-and-OpenStreetMap-Retrieval)
 
 ## 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)
@@ -1652,23 +1688,29 @@ Image augmentation is a technique used to expand a training dataset in order to
 * [cartoframes](https://github.com/CartoDB/cartoframes) -> integrate CARTO maps, analysis, and data services into data science workflows
 * [datashader](https://datashader.org/) -> create meaningful representations of large datasets quickly and flexibly. Read [Creating Visual Narratives from Geospatial Data Using Open-Source Technology Maxar blog post](https://blog.maxar.com/tech-and-tradecraft/2021/creating-visual-narratives-from-geospatial-data-using-open-source-technology)
 * [Kaleido](https://github.com/plotly/Kaleido) -> Fast static image export for web-based visualization libraries with zero dependencies
-* [flask-vector-tiles](https://github.com/mblackgeo/flask-vector-tiles) -> A simple Flask/leaflet based webapp for rendering vector tiles from PostGIS
 * [Embedding Projector in Wandb](https://docs.wandb.ai/ref/app/features/panels/weave/embedding-projector) -> allows users to plot multi-dimensional embeddings on a 2D plane using common dimension reduction algorithms like PCA, UMAP, and t-SNE
-* [flask-geocoding-webapp](https://github.com/mblackgeo/flask-geocoding-webapp) -> A quick example Flask application for geocoding and rendering a webmap using Folium/Leaflet
 * [PlotNeuralNet](https://github.com/HarisIqbal88/PlotNeuralNet) -> Latex code for making neural networks diagrams
 * [Damage Assessment Visualizer](https://github.com/microsoft/Nonprofits/tree/master/Damage%20Assessment%20Visualizer) -> leverages satellite imagery from a disaster region to visualize conditions of building and structures before and after a disaster
 * [NN-SVG](https://github.com/alexlenail/NN-SVG) -> is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually
-* [localtileserver](https://github.com/banesullivan/localtileserver) -> image tile server for viewing geospatial rasters with ipyleaflet, folium, or CesiumJS locally in Jupyter or remotely in Flask applications
-* [greppo](https://github.com/greppo-io/greppo) -> Build & deploy geospatial applications quick and easy. Read [Build a geospatial dashboard in Python using Greppo](https://towardsdatascience.com/build-a-geospatial-dashboard-in-python-using-greppo-60aff44ba6c9)
 * [bbox-visualizer](https://github.com/shoumikchow/bbox-visualizer) -> Make drawing and labeling bounding boxes easy as cake
 * [jupyter-bbox-widget](https://github.com/gereleth/jupyter-bbox-widget) -> A Jupyter widget for annotating images with bounding boxes
 * [EOmaps](https://github.com/raphaelquast/EOmaps) -> A library to create interactive maps of geographical datasets
 * [H3-Pandas](https://github.com/DahnJ/H3-Pandas) -> Integrates H3 with GeoPandas and Pandas
+* [gmplot](https://github.com/gmplot/gmplot) -> a matplotlib-like interface to render all the data you'd like on top of Google Maps
+
+## Web apps
+Flask is often used to serve up a simple web app based on templated HTML files
+* [FastMap](https://github.com/butlerbt/FastMap) -> Flask deployment of deep learning model performing segmentation task on aerial imagery building footprints
+* [Querying Postgres with Python Fastapi Backend and Leaflet-Geoman Frontend](https://geo.rocks/post/leaflet-geoman-fastapi-postgis/)
+* [cropcircles](https://github.com/doersino/cropcircles) -> a purely-client-side web app originally designed for accurately cropping circular center pivot irrigation fields from aerial imagery
 * [django-large-image](https://github.com/ResonantGeoData/django-large-image) -> Django endpoints for working with large images for tile serving
 * [Earth Classification API](https://github.com/conlamon/satellite-classification-flask-api) -> Flask based app that serves a CNN model and interfaces with a React and Leaflet front-end
 * [Demo flask map app](https://github.com/kdmayer/flask_tutorial) -> Building Python-based, database-driven web applications (with maps!) using Flask, SQLite, SQLAlchemy and MapBox
 * [Building a Web App for Instance Segmentation using Docker, Flask and Detectron2](https://towardsdatascience.com/instance-segmentation-web-app-63016b8ed4ae)
-* [gmplot](https://github.com/gmplot/gmplot) -> a matplotlib-like interface to render all the data you'd like on top of Google Maps
+* [greppo](https://github.com/greppo-io/greppo) -> Build & deploy geospatial applications quick and easy. Read [Build a geospatial dashboard in Python using Greppo](https://towardsdatascience.com/build-a-geospatial-dashboard-in-python-using-greppo-60aff44ba6c9)
+* [localtileserver](https://github.com/banesullivan/localtileserver) -> image tile server for viewing geospatial rasters with ipyleaflet, folium, or CesiumJS locally in Jupyter or remotely in Flask applications
+* [flask-geocoding-webapp](https://github.com/mblackgeo/flask-geocoding-webapp) -> A quick example Flask application for geocoding and rendering a webmap using Folium/Leaflet
+* [flask-vector-tiles](https://github.com/mblackgeo/flask-vector-tiles) -> A simple Flask/leaflet based webapp for rendering vector tiles from PostGIS
 
 ## Streamlit
 [Streamlit](https://streamlit.io/) is an awesome python framework for creating apps with python. Additionally they will host the apps free of charge. Here I list resources which are EO related. Note that a component is an addon which extends Streamlits basic functionality
@@ -1726,6 +1768,8 @@ Dask provides advanced parallelism and distributed out-of-core computation with
 * [pylandtemp](https://github.com/pylandtemp/pylandtemp) -> Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python
 * [sarsen](https://github.com/bopen/sarsen) -> Algorithms and utilities for Synthetic Aperture Radar (SAR) sensors
 * [sun-position](https://github.com/s-bear/sun-position) -> code for computing sun position
+* [simple_ortho](https://github.com/dugalh/simple_ortho) -> Fast and simple orthorectification of images with known DEM and camera model
+* [imageResolution](https://github.com/geojames/imageResolution) -> Simple spatial resolution calculator for nadir & oblique aerial imagery
 
 ## Julia language
 [Julia](https://julialang.org/) looks and feels a lot like Python, but can be much faster. Julia can call Python, C, and Fortran libraries and is capabale of C/Fortran speeds. Julia can be used in the familiar Jupyterlab notebook environment