Here, the strong and weak semantic zones set up the stable and uncertain description individually to determine a more stable semantic main body and uncertain semantic boundary of buildings. Next, a hierarchical disentangling strategy is set up to individually generate strong and weak semantic zones using a newly designed feature disentangling module (FDM). In this article, a novel hierarchical disentangling network with an encoder–decoder architecture called HDNet is proposed to consider both the stable and uncertain feature description in a convolution neural network (CNN). Thus, the above-mentioned situations would create large intra-class variances and poor inter-class discrimination, leading to uncertain feature descriptions for building extraction, which would result in over- or under-extraction phenomena. Additionally, with the spatial resolution of images increasing, there are diverse interior details and redundant context information present in building and background areas. However, buildings in different environments exhibit various scales, complicated spatial distributions, and different imaging conditions.
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To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.īuilding extraction using very high resolution (VHR) optical remote sensing imagery is an essential interpretation task that impacts human life. PDF is the official format for papers published in both, html and pdf forms.You may sign up for e-mail alerts to receive table of contents of newly released issues.Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.For some sites, the pre-pandemic NO 2 peak caused by local morning rush hour had still not returned in 2021, indicating a change in local traffic and commuter patterns.
Compared to the sharp decline in NO 2 emissions in 2020, the atmospheric NO 2 amounts in 2021 started to recover, but were still below the mean values of the pre-pandemic time.
Ground-based remote sensing data show a clear reduction in NO 2, especially in the more polluted downtown and airport areas (reductions from 35% to 40% in 2020 compared to 5-year mean value).
Meteorological and satellite data were used to facilitate the analysis and reveal detailed local emission changes from different areas of the City of Toronto. This work shows changes in tropospheric and surface nitrogen dioxide (NO 2) that were observed during first two years of the COVID-19 pandemic in the Greater Toronto Area, Canada.
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