The Experts below are selected from a list of 71733 Experts worldwide ranked by ideXlab platform
Stefano Ermon - One of the best experts on this subject based on the ideXlab platform.
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using Satellite Imagery to understand and promote sustainable development
Science, 2021Co-Authors: David B Lobell, Marshall Burke, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.
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using Satellite Imagery to understand and promote sustainable development
Social Science Research Network, 2020Co-Authors: Marshall Burke, David B Lobell, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models’ predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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using publicly available Satellite Imagery and deep learning to understand economic well being in africa
Nature Communications, 2020Co-Authors: Christopher Yeh, David B Lobell, Stefano Ermon, Anne Driscoll, Zhongyi Tang, Anthony Perez, George Azzari, Marshall BurkeAbstract:Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral Satellite Imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution Imagery, and comparison with independent wealth measurements from censuses suggests that errors in Satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime Imagery particularly useful in this task. We demonstrate the utility of Satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa’s most populous country. It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and Satellite Imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution Imagery with errors comparable to that of existing ground data.
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using Satellite Imagery to understand and promote sustainable development
National Bureau of Economic Research, 2020Co-Authors: David B Lobell, Marshall Burke, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models’ predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field.
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mapping missing population in rural india a deep learning approach with Satellite Imagery
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Jay Harshadbhai Patel, David B Lobell, Marshall Burke, Zoealanah Robert, Paul Novosad, Sam Asher, Zhongyi Tang, Stefano ErmonAbstract:Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite Imagery can provide sufficient information to build a population map without the cost and time of a government census. We present two Convolutional Neural Network (CNN) architectures which efficiently and effectively combine Satellite Imagery inputs from multiple sources to accurately predict the population density of a region. In this paper, we use Satellite Imagery from rural villages in India and population labels from the 2011 SECC census. Our best model achieves better performance than previous papers as well as LandScan, a community standard for global population distribution.
Marshall Burke - One of the best experts on this subject based on the ideXlab platform.
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wildfire smoke plume segmentation using geostationary Satellite Imagery
arXiv: Computer Vision and Pattern Recognition, 2021Co-Authors: Jeffrey L Wen, Marshall BurkeAbstract:Wildfires have increased in frequency and severity over the past two decades, especially in the Western United States. Beyond physical infrastructure damage caused by these wildfire events, researchers have increasingly identified harmful impacts of particulate matter generated by wildfire smoke on respiratory, cardiovascular, and cognitive health. This inference is difficult due to the spatial and temporal uncertainty regarding how much particulate matter is specifically attributable to wildfire smoke. One factor contributing to this challenge is the reliance on manually drawn smoke plume annotations, which are often noisy representations limited to the United States. This work uses deep convolutional neural networks to segment smoke plumes from geostationary Satellite Imagery. We compare the performance of predicted plume segmentations versus the noisy annotations using causal inference methods to estimate the amount of variation each explains in Environmental Protection Agency (EPA) measured surface level particulate matter <2.5um in diameter ($\textrm{PM}_{2.5}$).
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using Satellite Imagery to understand and promote sustainable development
Science, 2021Co-Authors: David B Lobell, Marshall Burke, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.
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using Satellite Imagery to understand and promote sustainable development
Social Science Research Network, 2020Co-Authors: Marshall Burke, David B Lobell, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models’ predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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using publicly available Satellite Imagery and deep learning to understand economic well being in africa
Nature Communications, 2020Co-Authors: Christopher Yeh, David B Lobell, Stefano Ermon, Anne Driscoll, Zhongyi Tang, Anthony Perez, George Azzari, Marshall BurkeAbstract:Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral Satellite Imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution Imagery, and comparison with independent wealth measurements from censuses suggests that errors in Satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime Imagery particularly useful in this task. We demonstrate the utility of Satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa’s most populous country. It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and Satellite Imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution Imagery with errors comparable to that of existing ground data.
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using Satellite Imagery to understand and promote sustainable development
National Bureau of Economic Research, 2020Co-Authors: David B Lobell, Marshall Burke, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models’ predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field.
Philippe C Baveye - One of the best experts on this subject based on the ideXlab platform.
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mapping invasive wetland plants in the hudson river national estuarine research reserve using quickbird Satellite Imagery
Remote Sensing of Environment, 2008Co-Authors: Magdeline Laba, Roger Michael Downs, Stephen C Smith, Sabrina Welsh, Chuck Neider, Susan White, Milo E Richmond, William D Philpot, Philippe C BaveyeAbstract:Abstract The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution Satellite Imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution Satellite Imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution Satellite Imagery offers significant potential for the mapping of invasive plant species in estuarine environments.
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mapping invasive wetland plants in the hudson river national estuarine research reserve using quickbird Satellite Imagery
Remote Sensing of Environment, 2008Co-Authors: Magdeline Laba, Stephen C Smith, Sabrina Welsh, Chuck Neider, Susan White, Milo E Richmond, William D Philpot, Roger Downs, Philippe C BaveyeAbstract:Abstract The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution Satellite Imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution Satellite Imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution Satellite Imagery offers significant potential for the mapping of invasive plant species in estuarine environments.
David B Lobell - One of the best experts on this subject based on the ideXlab platform.
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using Satellite Imagery to understand and promote sustainable development
Science, 2021Co-Authors: David B Lobell, Marshall Burke, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.
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using Satellite Imagery to understand and promote sustainable development
Social Science Research Network, 2020Co-Authors: Marshall Burke, David B Lobell, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models’ predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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using publicly available Satellite Imagery and deep learning to understand economic well being in africa
Nature Communications, 2020Co-Authors: Christopher Yeh, David B Lobell, Stefano Ermon, Anne Driscoll, Zhongyi Tang, Anthony Perez, George Azzari, Marshall BurkeAbstract:Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral Satellite Imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution Imagery, and comparison with independent wealth measurements from censuses suggests that errors in Satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime Imagery particularly useful in this task. We demonstrate the utility of Satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa’s most populous country. It is generally difficult to scale derived estimates and understand the accuracy across locations for passively-collected data sources, such as mobile phones and Satellite Imagery. Here the authors show that their trained deep learning models are able to explain 70% of the variation in ground-measured village wealth in held-out countries, outperforming previous benchmarks from high-resolution Imagery with errors comparable to that of existing ground data.
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using Satellite Imagery to understand and promote sustainable development
National Bureau of Economic Research, 2020Co-Authors: David B Lobell, Marshall Burke, Anne Driscoll, Stefano ErmonAbstract:Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses Satellite Imagery to understand these outcomes, with a focus on approaches that combine Imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution (spatial, temporal, and spectral) of Satellite Imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models’ predictive performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight key research directions for the field.
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mapping missing population in rural india a deep learning approach with Satellite Imagery
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Jay Harshadbhai Patel, David B Lobell, Marshall Burke, Zoealanah Robert, Paul Novosad, Sam Asher, Zhongyi Tang, Stefano ErmonAbstract:Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite Imagery can provide sufficient information to build a population map without the cost and time of a government census. We present two Convolutional Neural Network (CNN) architectures which efficiently and effectively combine Satellite Imagery inputs from multiple sources to accurately predict the population density of a region. In this paper, we use Satellite Imagery from rural villages in India and population labels from the 2011 SECC census. Our best model achieves better performance than previous papers as well as LandScan, a community standard for global population distribution.
Magdeline Laba - One of the best experts on this subject based on the ideXlab platform.
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mapping invasive wetland plants in the hudson river national estuarine research reserve using quickbird Satellite Imagery
Remote Sensing of Environment, 2008Co-Authors: Magdeline Laba, Roger Michael Downs, Stephen C Smith, Sabrina Welsh, Chuck Neider, Susan White, Milo E Richmond, William D Philpot, Philippe C BaveyeAbstract:Abstract The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution Satellite Imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution Satellite Imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution Satellite Imagery offers significant potential for the mapping of invasive plant species in estuarine environments.
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mapping invasive wetland plants in the hudson river national estuarine research reserve using quickbird Satellite Imagery
Remote Sensing of Environment, 2008Co-Authors: Magdeline Laba, Stephen C Smith, Sabrina Welsh, Chuck Neider, Susan White, Milo E Richmond, William D Philpot, Roger Downs, Philippe C BaveyeAbstract:Abstract The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution Satellite Imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution Satellite Imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution Satellite Imagery offers significant potential for the mapping of invasive plant species in estuarine environments.