Update README.md

README.md

@@ -306,6 +306,7 @@ Classification
 * [cultionet](https://github.com/jgrss/cultionet) -> segmentation of cultivated land, built on PyTorch Geometric and PyTorch Lightning
 * [sentinel-tree-cover](https://github.com/wri/sentinel-tree-cover) -> code for 2020 [paper](https://arxiv.org/abs/2005.08702): A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery
 * [crop-type-detection-ICLR-2020](https://github.com/RadiantMLHub/crop-type-detection-ICLR-2020) -> Winning Solutions from Crop Type Detection Competition at CV4A workshop, ICLR 2020
+* [Crop identification using satellite imagery](https://write.agrevolution.in/crop-identification-using-satellite-imagery-introduction-83d79344f9ee) -> Medium article, introduction to crop identification
 
 ### Segmentation - Water, coastlines & floods
 * [UNSOAT used fastai to train a Unet to perform semantic segmentation on satellite imageries to detect water](https://forums.fast.ai/t/unosat-used-fastai-ai-for-their-floodai-model-discussion-on-how-to-move-forward/78468) - [paper](https://www.mdpi.com/2072-4292/12/16/2532) + [notebook](https://github.com/UNITAR-UNOSAT/UNOSAT-AI-Based-Rapid-Mapping-Service/blob/master/Fastai%20training.ipynb), accuracy 0.97, precision 0.91, recall 0.92
@@ -474,7 +475,8 @@ Semantic Segmentation
 * [GeoSeg](https://github.com/WangLibo1995/GeoSeg) -> code for 2022 [paper](https://www.sciencedirect.com/science/article/pii/S0924271622001654): UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
 * [BESNet](https://github.com/FlyC235/BESNet) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/7/1638): BES-Net: Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation. Applied to Vaihingen and Potsdam datasets
 * [CVNet](https://github.com/xzq-njust/CVNet) -> code for 2022 paper: CVNet: Contour Vibration Network for Building Extraction
-* [CFENet](https://github.com/djzgroup/CFENet) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/9/2276): A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery 
+* [CFENet](https://github.com/djzgroup/CFENet) -> code for 2022 [paper](https://www.mdpi.com/2072-4292/14/9/2276): A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery
+* [HiSup](https://github.com/SarahwXU/HiSup) -> code for 2022 [paper](https://arxiv.org/abs/2208.00609): Accurate Polygonal Mapping of Buildings in Satellite Imagery
 
 ### Segmentation - Solar panels
 * [DeepSolar](https://github.com/wangzhecheng/DeepSolar) -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. [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. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in ..  Virginia](https://github.com/bessammehenni/DeepSolar_adoption_Virginia) and [DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping](https://github.com/gabrielkasmi/dsfrance)
@@ -1647,6 +1649,7 @@ This section contains a short list of datasets relevant to deep learning, partic
 * [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
+* [Sentinel-2 Cloud Cover Segmentation Dataset](https://mlhub.earth/data/ref_cloud_cover_detection_challenge_v1) on Radiant mlhub
 
 ## Landsat
 * Long running US program -> see [Wikipedia](https://en.wikipedia.org/wiki/Landsat_program)
@@ -1938,7 +1941,8 @@ Since there is a whole community around GEE I will not reproduce it here but lis
 * [Multi-modality-image-matching](https://github.com/StaRainJ/Multi-modality-image-matching-database-metrics-methods) -> image matching dataset including several remote sensing modalities
 * [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
-* [QXS-SAROPT](https://github.com/yaoxu008/QXS-SAROPT) -> Optical and SAR pairing dataset from the [paper](https://arxiv.org/abs/2103.08259): The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion 
+* [QXS-SAROPT](https://github.com/yaoxu008/QXS-SAROPT) -> Optical and SAR pairing dataset from the [paper](https://arxiv.org/abs/2103.08259): The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion
+* [SAR-ACD](https://github.com/AICyberTeam/SAR-ACD) -> SAR-ACD consists of 4322 aircraft clips with 6 civil aircraft categories and 14 other aircraft categories
 
 ## Kaggle
 Kaggle hosts over > 200 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets).