Ryan N. Engstrom
Orcid: 0000-0002-3063-0551
According to our database1,
Ryan N. Engstrom
authored at least 20 papers
between 2011 and 2024.
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Bibliography
2024
Towards a scalable and transferable approach to map deprived areas using Sentinel-2 images and machine learning.
Comput. Environ. Urban Syst., 2024
Proceedings of the IGARSS 2024, 2024
Large Area Mapping of Urban Deprivation from Sentinel-2 and Google Open Buildings using Deep Learning.
Proceedings of the IGARSS 2024, 2024
2023
Evaluating the Ability to Use Contextual Features to Map Deprived Areas 'Slums' in Multiple Cities.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023
2022
A Methodology for Georeferencing and Mosaicking Corona Imagery in Semi-Arid Environments.
Remote. Sens., 2022
2021
Evaluating the Ability to Use Contextual Features Derived from Multi-Scale Satellite Imagery to Map Spatial Patterns of Urban Attributes and Population Distributions.
Remote. Sens., 2021
Open data for algorithms: mapping poverty in Belize using open satellite derived features and machine learning.
Inf. Technol. Dev., 2021
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021
2020
The Role of Earth Observation in an Integrated Deprived Area Mapping "System" for Low-to-Middle Income Countries.
Remote. Sens., 2020
2019
Proceedings of the Joint Urban Remote Sensing Event, 2019
Evaluating the Relationship Between Contextual Features Derived from Very High Spatial Resolution Imagery and Urban Attributes: A Case Study in Sri Lanka.
Proceedings of the Joint Urban Remote Sensing Event, 2019
2018
Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine.
Remote. Sens., 2018
2017
Evaluating the relationship between spatial and spectral features derived from high spatial resolution satellite data and urban poverty in Colombo, Sri Lanka.
Proceedings of the Joint Urban Remote Sensing Event, 2017
2016
Determining the Relationship Between Census Data and Spatial Features Derived From High-Resolution Imagery in Accra, Ghana.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2016
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016
Evaluating the use of multiple imagery-derived spatial features to predict census demographic variables in Accra, Ghana.
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016
2015
Proceedings of the Joint Urban Remote Sensing Event, 2015
Assessing the relationship between spatial features derived from high resolution satellite imagery and census variables in Accra, Ghana.
Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015
2011
Do the most vulnerable people live in the worst slums? A spatial analysis of Accra, Ghana.
Ann. GIS, 2011
Using remotely sensed data to map variability in health and wealth indicators in Accra, Ghana.
Proceedings of the Joint Urban Remote Sensing Event, 2011