Towards Indirect Top-Down Road Transport Emissions Estimation

  title={Towards Indirect Top-Down Road Transport Emissions Estimation},
  author={Ryan Mukherjee and Derek Rollend and Gordon A. Christie and Armin Hadzic and Sally Matson and A. Saksena and Marisa Hughes},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
Road transportation is one of the largest sectors of greenhouse gas (GHG) emissions affecting climate change. Tackling climate change as a global community will require new capabilities to measure and inventory road transport emissions. However, the large scale and distributed nature of vehicle emissions make this sector especially challenging for existing inventory methods. In this work, we develop machine learning models that use satellite imagery to perform indirect top-down estimation of… Expand

Figures and Tables from this paper


Cities, traffic, and CO2: A multidecadal assessment of trends, drivers, and scaling relationships
A highly nonlinear relationship between population density and emissions is found, and large biases in regional estimates of CO2 from inventories that rely on population as a linear predictor of vehicle activity are identified. Expand
Quantification of fossil fuel CO2 emissions on the building/street scale for a large U.S. city.
This research effort is the first to use bottom-up methods to quantify all fossil fuel CO(2) emissions down to the scale of individual buildings, road segments, and industrial/electricity production facilities on an hourly basis for an entire urban landscape. Expand
Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach
A deep convolutional neural network is employed to process the imagery by extracting image features that characterize the day-to-day dynamic changes in the built environment and more importantly the image colors related to aerosol loading, and a random forest regressor is used to estimate PM2.5 based on the extracted image features along with meteorological conditions. Expand
The Open-source Data Inventory for Anthropogenic Carbon dioxide (CO2), version 2016 (ODIAC2016): A global, monthly fossil-fuel CO2 gridded emission data product for tracer transport simulations and surface flux inversions.
The year 2016 version of the ODIAC emission data product (ODIAC2016) is described and analyses that help guiding data users, especially for atmospheric CO2 tracer transport simulations and flux inversion analysis are presented. Expand
A very high-resolution (1 km×1 km) global fossil fuel CO 2 emission inventory derived using a point source database and satellite observations of nighttime lights
Abstract. Emissions of CO 2 from fossil fuel combustion are a critical quantity that must be accurately given in established flux inversion frameworks. Work with emerging satellite-based inversionsExpand
EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012
Abstract. The Emissions Database for Global Atmospheric Research (EDGAR) compiles anthropogenic emissions data for greenhouse gases (GHGs), and for multiple air pollutants, based on internationalExpand
High resolution temporal profiles in the Emissions Database for Global Atmospheric Research
This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises. Expand
On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals
A spatio-temporal statistical modelling framework known as fixed rank kriging (FRK) is used to obtain global predictions and prediction standard errors of column-averaged carbon dioxide based on Version 7r and Version 8r retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite. Expand
SpaceNet: A Remote Sensing Dataset and Challenge Series
It is proposed that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. Expand
Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery
xBD provides pre- and post-event multi-band satellite imagery from a variety of disaster events with building polygons, classification labels for damage types, ordinal labels of damage level, and corresponding satellite metadata, and will be the largest building damage assessment dataset to date. Expand