Remove dead README links

Author: robmarkcole

README.md

@@ -326,8 +326,6 @@ Note that deforestation detection may be treated as a segmentation task or a cha
 
 - [TOFMapper](https://github.com/Moerizzy/TOFMapper) -> a semantic segmentation tool for mapping and classifying Trees outside Forest in high resolution aerial images
 
-- [SERA-H](https://github.com/ThomasBoudras/SERA-H) -> High-resolution (2.5m) canopy height mapping from Sentinel-1 & Sentinel-2 time series. Combines EDSR super-resolution and UTAE temporal attention, trained with ALS supervision, to achieve SOTA accuracy using only open-access sentinel satellite data.
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 ### Segmentation - Water, coastlines, rivers & floods
 
 - [sat-water](https://github.com/busayojee/sat-water) -> Semantic segmentation of water bodies in satellite imagery, producing pixel-wise water masks from remote sensing images using a U-Net–style deep learning pipeline (data preparation, training, inference, and evaluation).
@@ -1206,8 +1204,6 @@ Detecting the most noticeable or important object in a scene
 
 - [satellite_image_tinhouse_detector](https://github.com/yasserius/satellite_image_tinhouse_detector) -> Detection of tin houses from satellite/aerial images using the Tensorflow Object Detection API
 
-- [Machine Learning For Rooftop Detection and Solar Panel Installment](https://www.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://www.omdena.com/blog/rooftops-classification)
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 - [Building Extraction with YOLT2 and SpaceNet Data](https://medium.com/the-downlinq/building-extraction-with-yolt2-and-spacenet-data-a926f9ffac4f)
 
 - [XBD-hurricanes](https://github.com/dbuscombe-usgs/XBD-hurricanes) -> Models for building (and building damage) detection in high-resolution (<1m) satellite and aerial imagery using a modified RetinaNet model
@@ -1311,8 +1307,6 @@ Detecting the most noticeable or important object in a scene
 
 - [CarCounting](https://github.com/JacksonPeoples/CarCounting) -> using Yolov3 & COWC dataset
 
-- [Traffic density estimation as a regression problem instead of object detection](https://www.omdena.com/blog/ai-road-safety)
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 - [Rotation-EfficientDet-D0](https://github.com/HsLOL/Rotation-EfficientDet-D0) -> PyTorch implementation of Rotated EfficientDet, applied to a custom rotation vehicle dataset (car counting)
 
 - [RSVC2021-Dataset](https://github.com/YinongGuo/RSVC2021-Dataset) -> A dataset for Vehicle Counting in Remote Sensing images, created from the DOTA & ITCVD
@@ -3013,8 +3007,6 @@ Self-supervised, unsupervised & contrastive learning are all methods of machine
 
 - [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)
 
-- [Unsupervised Learning for Land Cover Classification in Satellite Imagery](https://www.omdena.com/blog/land-cover-classification)
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 - [Tile2Vec: Unsupervised representation learning for spatially distributed data](https://ermongroup.github.io/blog/tile2vec/)
 
 - [Contrastive Sensor Fusion](https://github.com/descarteslabs/contrastive_sensor_fusion) -> Code implementing Contrastive Sensor Fusion, an approach for unsupervised learning of multi-sensor representations targeted at remote sensing imagery