Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning.

ABSTRACT: Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM_2.5) and noise levels in Accra, Ghana. Data used for training th

SUBMITTER: Nathvani R 

PROVIDER: S-EPMC7615099 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature