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Found 5 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.
AI for bureaucratic productivity: Measuring the potential of AI to help automate 143 million UK government transactions
We estimate that UK central government conducts approximately one billion citizen-facing transactions per year in the provision of around 400 services, of which approximately 143 million are complex repetitive transactions.
Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data
These `cheaper' learning techniques hold significant potential for the social sciences, where development of large labelled training datasets is often a significant practical impediment to the use of machine learning for analytical tasks.
A multidomain relational framework to guide institutional AI research and adoption
Calls for new metrics, technical standards and governance mechanisms to guide the adoption of Artificial Intelligence (AI) in institutions and public administration are now commonplace.
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].
