The prophesied circulation of fleets of autonomous vehicles (AVs) in urban and rural environments promises unprecedented opportunities to remotely sense streetscapes at fine-grain spatial and temporal resolution. AVs employ a variety of on-board sensors to capture information about the local environs for the primary purpose of vehicular navigation. However, we propose that these data may find further secondary use in a broad array of breakthrough applications: technologies and use cases that are enabled through the fine-grain spatio-temporal sensing of the lived environment. Consequently, a market for the secondary use of AV-collected data is emergent and a cloud-based architecture to manage the collection, processing, and communication of AV-derived data is required. Excitingly, the application of machine learning models to extract desirable secondary information from these fine-grain spatio-temporal data will enable unprecedented global-scale and time-series studies. Herein, we outline our vision for the utility of a Remote sensing AV-based Informatics Layer (RAIL) and the breakthrough applications it would enable. We define our vision based on recent and relevant trends in AV technology, discuss anticipated applications, discuss key technical considerations, and explore theoretical economic models for the exposed API. We conclude with discussion of the socio-technical ramifications of this system.

We present MetaHate, a NLP meta-model for detecting hatefulness in tweets by combining predictors for hate, emotion, sentiment, and offensiveness. We evaluate this model with the TweetEval benchmark for hate speech detection. MetaHate improves the baseline TweetEval RoBERTa based model on the TweetEval benchmark. Optimizing the decision threshold for the macro-averaged F1-score, MetaHate achieves a F1-score of 0.70, while the TweetEval RoBERTa-Twitter Retrained Hate model achieves a F1-score of 0.63. This improvement on one of the most difficult tasks on the TweetEval benchmark was achieved with no additional training data and negligible computational time and cost. MetaHate demonstrates the utility of leveraging predictions from language models trained for various tasks to improve performance on a single task.