Update README.md

README.md

@@ -471,7 +471,8 @@ Semantic Segmentation
 * [UBC-dataset](https://github.com/AICyberTeam/UBC-dataset) -> a dataset for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings
 * [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 Vibratioin Network for Building Extraction
+* [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 
 
 ### 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)
@@ -1917,6 +1918,7 @@ 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
 * [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
 
 ## Kaggle
 Kaggle hosts over > 200 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets).
@@ -2532,6 +2534,7 @@ Since raw images can be very large, it is usually necessary to chip/tile them in
 * [rpc_cropper](https://github.com/carlodef/rpc_cropper) -> A small standalone tool to crop satellite images and their RPC
 * [geotile](https://github.com/iamtekson/geotile) -> python library for tiling the geographic raster data
 * [GeoPatch](https://github.com/Hejarshahabi/GeoPatch) -> generating patches from remote sensing data
+* [ImageTilingUtils](https://github.com/vfdev-5/ImageTilingUtils) -> Minimalistic set of image reader agnostic tools to easily iterate over large images
 
 ## Image dataset creation
 Many datasets on kaggle & elsewhere have been created by screen-clipping Google Maps or browsing web portals. The tools below are to create datasets programatically
@@ -2566,6 +2569,7 @@ Many datasets on kaggle & elsewhere have been created by screen-clipping Google
 * [Aerial-Satellite-Imagery-Retrieval](https://github.com/chiragkhandhar/Aerial-Satellite-Imagery-Retrieval) -> A program using Bing maps tile system to automatically download Aerial / Satellite Imagery given a lat/lon bounding box and level of detail
 * [google-maps-at-88-mph](https://github.com/doersino/google-maps-at-88-mph) -> Google Maps keeps old satellite imagery around for a while – this tool collects what's available for a user-specified region in the form of a GIF
 * [srtmDownloader](https://github.com/Abdi-Ghasem/srtmDownloader) -> Python library (multi-threaded) for retrieving SRTM elevation map of CGIAR-CSI
+* [ImageDatasetViz](https://github.com/vfdev-5/ImageDatasetViz) -> create a mosaic of images in a dataset for previewing purposes
 
 ## Image augmentation packages
 Image augmentation is a technique used to expand a training dataset in order to improve ability of the model to generalise