remove BEGINNER

README.md

@@ -94,7 +94,7 @@ Classification is a fundamental task in remote sensing data analysis, where the
    1.1. Land classification on Sentinel 2 data using a [simple sklearn cluster algorithm](https://github.com/acgeospatial/Satellite_Imagery_Python/blob/master/Clustering_KMeans-Sentinel2.ipynb) or [deep learning CNN](https://towardsdatascience.com/land-use-land-cover-classification-with-deep-learning-9a5041095ddb)
 
    1.2. Land Use Classification on Merced dataset using CNN [in Keras](https://github.com/tavgreen/landuse_classification)
-  or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial-data-applications-multi-label-classification-2b0a1838fcf3). Also checkout [Multi-label Land Cover Classification](https://towardsdatascience.com/multi-label-land-cover-classification-with-deep-learning-d39ce2944a3d) using the redesigned multi-label Merced dataset with 17 land cover classes `BEGINNER`
+  or [fastai](https://medium.com/spatial-data-science/deep-learning-for-geospatial-data-applications-multi-label-classification-2b0a1838fcf3). Also checkout [Multi-label Land Cover Classification](https://towardsdatascience.com/multi-label-land-cover-classification-with-deep-learning-d39ce2944a3d) using the redesigned multi-label Merced dataset with 17 land cover classes
 
    1.3. [Multi-Label Classification of Satellite Photos of the Amazon Rainforest using keras](https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/) or [FastAI](https://towardsdatascience.com/fastai-multi-label-image-classification-8034be646e95)
 
@@ -704,7 +704,7 @@ Extracting roads is challenging due to the occlusions caused by other objects an
 
 ### 2.9. Segmentation - Buildings & rooftops
 
-  2.9.1. [Road and Building Semantic Segmentation in Satellite Imagery](https://github.com/Paulymorphous/Road-Segmentation) uses U-Net on the Massachusetts Roads Dataset & keras `BEGINNER`
+  2.9.1. [Road and Building Semantic Segmentation in Satellite Imagery](https://github.com/Paulymorphous/Road-Segmentation) uses U-Net on the Massachusetts Roads Dataset & keras
 
   2.9.2. [find-unauthorized-constructions-using-aerial-photography](https://medium.com/towards-artificial-intelligence/find-unauthorized-constructions-using-aerial-photography-and-deep-learning-with-code-part-2-b56ca80c8c99) -> semantic segmentation using U-Net with custom_f1 metric & Keras. The creation of the dataset is described in [this article](https://pub.towardsai.net/find-unauthorized-constructions-using-aerial-photography-and-deep-learning-with-code-part-1-6d3ca7ff6fa0)
 
@@ -1098,7 +1098,7 @@ In instance segmentation, each individual 'instance' of a segmented area is give
 
   3.2. [Instance segmentation of center pivot irrigation system in Brazil](https://github.com/saraivaufc/instance-segmentation-maskrcnn) using free Landsat images, mask R-CNN & Keras
 
-  3.3. [Building-Detection-MaskRCNN](https://github.com/Mstfakts/Building-Detection-MaskRCNN) -> Building detection from the SpaceNet dataset by using Mask RCNN `BEGINNER`
+  3.3. [Building-Detection-MaskRCNN](https://github.com/Mstfakts/Building-Detection-MaskRCNN) -> Building detection from the SpaceNet dataset by using Mask RCNN
 
   3.4. [Oil tank instance segmentation with Mask R-CNN](https://github.com/georgiosouzounis/instance-segmentation-mask-rcnn) with [accompanying article](https://medium.com/@georgios.ouzounis/oil-storage-tank-instance-segmentation-with-mask-r-cnn-77c94433045f) using Keras & Airbus Oil Storage Detection Dataset on Kaggle
 
@@ -1423,7 +1423,7 @@ Detecting the most noticeable or important object in a scene
 
   4.7.1. [Detection of parkinglots and driveways with retinanet](https://github.com/spiyer99/retinanet)
 
-  4.7.2. [pytorch-vedai](https://github.com/MichelHalmes/pytorch-vedai) -> object detection on the VEDAI dataset: Vehicle Detection in Aerial Imagery `BEGINNER`
+  4.7.2. [pytorch-vedai](https://github.com/MichelHalmes/pytorch-vedai) -> object detection on the VEDAI dataset: Vehicle Detection in Aerial Imagery
 
   4.7.3. [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. Also see [S2TD](https://github.com/hfisser/S2TD)
 
@@ -1533,9 +1533,9 @@ A variety of techniques can be used to count animals, including object detection
 
 ### 4.12. Object detection - Miscellaneous
 
-  4.12.1. [Object detection on Satellite Imagery using RetinaNet](https://medium.com/@ije_good/object-detection-on-satellite-imagery-using-retinanet-part-1-training-e589975afbd5) -> using the Kaggle Swimming Pool and Car Detection dataset `BEGINNER`
+  4.12.1. [Object detection on Satellite Imagery using RetinaNet](https://medium.com/@ije_good/object-detection-on-satellite-imagery-using-retinanet-part-1-training-e589975afbd5) -> using the Kaggle Swimming Pool and Car Detection dataset
 
-  4.12.2. [Tackling the Small Object Problem in Object Detection](https://blog.roboflow.com/tackling-the-small-object-problem-in-object-detection) `BEGINNER`
+  4.12.2. [Tackling the Small Object Problem in Object Detection](https://blog.roboflow.com/tackling-the-small-object-problem-in-object-detection)
 
   4.12.3. [Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review](https://www.mdpi.com/2072-4292/12/10/1667)