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To train the model, he identified known locations of tree canopy using lidar data and NAIP imagery over California. Using that as ground truth, the model was trained to classify which pixels contain trees in the corresponding satellite images. The result is a machine-learning model that has learned to identify trees just using four-band high-resolution (~1 meter) satellite or aerial imagery—no lidar required! — Medium
Former New York Times cartographer Tim Wallace describes how his current firm, Santa Fe-based Descartes Labs, has built a machine learning model to identify tree canopy from satellite imagery thus making accurate mapping of trees and urban forests far more accessible to cities worldwide. San... View full entry
Justin O'Beirne lays out years worth of research on mapping technologies in his essay Google Map's Moat. O'Beirne reveals,"Over the past year, we’ve been comparing Google Maps and Apple Maps [...] The biggest difference is the building footprints: Google seems to have them all, while Apple... View full entry
We’ve stripped out the street names and lost the labels – but can you still recognise the cities from their aerial views? — theguardian.com
This exercise in aerial recognition comes in quiz form, where the viewer must guess the city pictured in a monochrome-treated satellite image of an urban grid. Identifying some cities is far easier than others – the quiz will tell you how your response stacks up against others'. View full entry