NYC Building Plume Detection from Skyline Imagery, 2013-2015
by Ben Steers / New York University
Available on 1 platform
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Description
Ben Steers at New York University's Center for Urban Science and Progress created a dataset of over 1,100 annotated plumes from Manhattan building emissions. The dataset was generated by applying a trained deep convolutional neural network to archival skyline imagery captured at 0.1 Hz by the Urban Observatory. It contains detections of plumes, classified by color, from two periods: October 26 to December 31, 2013, and January 1 to March 13, 2015.
Use Cases
Training object detection models for building plume emissions based on annotated plume imagery.
Analyzing urban pollution sources and activity patterns based on plume ejection rates.
Classifying plume types (e.g., carbon vs. water vapor) based on color features mentioned in the description.
Monitoring environmental impacts of urban metabolism over time using the provided temporal coverage.
Strengths
Contains over 1,100 actual annotated plumes for training.
Includes sources of contamination (clouds, shadows, lights) to improve model robustness.
Covers two distinct time periods spanning parts of 2013 and 2015.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment.
Data may reflect geographic bias inherent to the Manhattan-only imagery.
Provenance
Source
New York University's Center for Urban Science and Progress (CUSP-UO)
Collection Method
Deep convolutional neural network applied to continuous skyline imagery from the Urban Observatory.
Time Range
October 26, 2013 to December 31, 2013; January 1, 2015 to March 13, 2015
Geography
Manhattan, New York City, USA
License is listed as Open Access (green); specific usage terms should be verified.