remove dead

Author: robmarkcole

README.md

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 * If model performance in unsatisfactory, try to increase your dataset size before switching to exotic model architectures
 * In training, whenever possible increase the batch size, as small batch sizes produce poor normalization statistics
 * The vast majority of the literature uses supervised learning with the requirement for large volumes of annotated data, which is a bottleneck to development and deployment. We are just starting to see self-supervised approaches applied to remote sensing data
-* [4-ways-to-improve-class-imbalance](https://towardsdatascience.com/4-ways-to-improve-class-imbalance-for-image-data-9adec8f390f1) discusses the pros and cons of several rebalancing techniques, applied to an aerial dataset. Reason to read: models can reach an accuracy ceiling where majority classes are easily predicted but minority classes poorly predicted. Overall model accuracy may not improve until steps are taken to account for class imbalance.
 * For general guidance on dataset size see [this issue](https://github.com/ultralytics/yolov5/issues/3306)
 * Read [A Recipe for Training Neural Networks](http://karpathy.github.io/2019/04/25/recipe/) by Andrej Karpathy
 * [Seven steps towards a satellite imagery dataset](https://omdena.com/blog/satellite-imagery-dataset/)
@@ -68,7 +67,6 @@ A number of metrics are common to all model types (but can have slightly differe
 * `IoU` is an object detection specific metric, being the average intersect over union of prediction and ground truth bounding boxes for a given confidence threshold
 * `mAP@0.5` is another object detection specific metric, being the mean value of the average precision for each class. `@0.5` sets a threshold for how much of the predicted bounding box overlaps the ground truth bounding box, i.e. "minimum 50% overlap"
 * For more comprehensive definitions checkout [Object-Detection-Metrics](https://github.com/rafaelpadilla/Object-Detection-Metrics)
-* [Metrics to Evaluate your Semantic Segmentation Model](https://towardsdatascience.com/metrics-to-evaluate-your-semantic-segmentation-model-6bcb99639aa2)
 
 # Cloud GPUs
 A GPU is required for training deep learning models (but not necessarily for inferencing), and this section lists a couple of free Jupyter environments with GPU available. There is a good overview of online Jupyter development environments [on the fastai site](https://course19.fast.ai). For personal projects I have historically used Google Colab with data hosted on Google Drive. The landscape for GPU providers is constantly changing. I currently recommend [lightning.ai](https://lightning.ai/) or AWS
@@ -220,7 +218,6 @@ Flask is often used to serve up a simple web app that can expose a ML model
 * [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)
 * [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. Checkout [bokeh-tiler](https://github.com/avanetten/bokeh-tiler) 
 * [flask-geocoding-webapp](https://github.com/mblackgeo/flask-geocoding-webapp) -> A quick example Flask application for geocoding and rendering a webmap using Folium/Leaflet
@@ -237,7 +234,6 @@ Processing on board a satellite allows less data to be downlinked. e.g. super-re
 * [Lockheed Martin and USC to Launch Jetson-Based Nanosatellite for Scientific Research Into Orbit - Aug 2020](https://news.developer.nvidia.com/lockheed-martin-usc-jetson-nanosatellite/) - One app that will run on the GPU-accelerated satellite is SuperRes, an AI-based application developed by Lockheed Martin, that can automatically enhance the quality of an image.
 * [Intel to place movidius in orbit to filter images of clouds at source - Oct 2020](https://techcrunch.com/2020/10/20/intel-is-providing-the-smarts-for-the-first-satellite-with-local-ai-processing-on-board/) - Getting rid of these images before they’re even transmitted means that the satellite can actually realize a bandwidth savings of up to 30%
 * [WorldFloods](https://watchers.news/2021/07/11/worldfloods-ai-pioneered-at-oxford-for-global-flood-mapping-launches-into-space/) will pioneer the detection of global flood events from space, launched on June 30, 2021. [This paper](https://arxiv.org/pdf/1910.03019.pdf) describes the model which is run on Intel Movidius Myriad2 hardware capable of processing a 12 MP image in less than a minute
-* [How AI and machine learning can support spacecraft docking](https://towardsdatascience.com/deep-learning-in-space-964566f09dcd) with [repo](https://github.com/nevers/space-dl) uwing Yolov3
 * [Sony’s Spresense microcontroller board is going to space](https://developer.sony.com/posts/the-spresense-microcontroller-board-launched-in-space/) -> vision applications include cloud detection, [more details here](https://www.hackster.io/dhruvsheth_/to-space-and-beyond-with-edgeimpulse-and-sony-s-spresense-d87a70)
 * [Palantir Edge AI in Space](https://blog.palantir.com/edge-ai-in-space-93d793433a1e) -> using NVIDIA Jetson for ship/aircraft/cloud detection & land cover segmentation
 * [Spiral Blue](https://spiralblue.space/) -> startup building edge computers to run AI analytics on-board satellites