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Found 4 papers, 2 papers with code
Quantifying Urban Canopy Cover with Deep Convolutional Neural Networks
Urban canopy cover is important to mitigate the impact of climate change.
Treepedia 2.0: Applying Deep Learning for Large-scale Quantification of Urban Tree Cover
We apply state-of-the-art deep learning models, and compare their performance to a previously established benchmark of an unsupervised method.
A novel method for predicting and mapping the presence of sun glare using Google Street View
GSV images have view sight similar to drivers, which would make GSV images suitable for estimating the visibility of sun glare to drivers.
Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images
The method combines the state-of-the-art DL object detection algorithm (i. e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV.
