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Found 7 papers, 1 papers with code
Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction
We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning.
Self-supervised learning unveils change in urban housing from street-level images
Cities around the world face a critical shortage of affordable and decent housing.
Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery
Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2].
Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data
Back in the 1970s, Jane Jacobs theorized urban vitality and found that there are four conditions required for the promotion of life in cities: diversity of land use, small block sizes, the mix of economic activities, and concentration of people.
Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball
We present a simple regularization of adversarial perturbations based upon the perceptual loss.
An unsupervised approach to Geographical Knowledge Discovery using street level and street network images
Researches integrating geographical processes in machine learning models and the use of unsupervised approacheson geographical data for knowledge discovery had been sparse.
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective.
