remove flaky link

Author: robmarkcole

README.md

@@ -1720,7 +1720,6 @@ This section contains a short list of datasets relevant to deep learning, partic
 ## Microsoft
 * [US Building Footprints](https://github.com/Microsoft/USBuildingFootprints) -> building footprints in all 50 US states, GeoJSON format, generated using semantic segmentation. Also [Australia](https://github.com/microsoft/AustraliaBuildingFootprints), [Canadian](https://github.com/Microsoft/CanadianBuildingFootprints), [Uganda-Tanzania](https://github.com/microsoft/Uganda-Tanzania-Building-Footprints), [Kenya-Nigeria](https://github.com/microsoft/KenyaNigeriaBuildingFootprints) and [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints) are available. Checkout [RasterizingBuildingFootprints](https://github.com/mehdiheris/RasterizingBuildingFootprints) to convert vector shapefiles to raster layers
 * [Microsoft Planetary Computer](https://innovation.microsoft.com/en-us/planetary-computer) is a Dask-Gateway enabled JupyterHub deployment focused on supporting scalable geospatial analysis, [source repo](https://github.com/microsoft/planetary-computer-hub)
-* [Ai for Earth program](https://www.microsoft.com/en-us/ai/ai-for-earth)
 * [landcover-orinoquia](https://github.com/microsoft/landcover-orinoquia) -> Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project
 * [RoadDetections dataset by Microsoft](https://github.com/microsoft/RoadDetections)