Big Data has been playing a major role in the domain of Deep Learning applications as many companies and institutions continue to find solutions and extract certain trends in fields of climate change, weather forecasting and meteorology. This project extracts weather events data from multiple data sources that are supported by National Centers for Environmental information (NCEI) [1] and Amazon Web Services (AWS) [2]. Data sources include Next-Generation NEXRAD [3] Doppler radar reflectivity, GOES-16 [4] multi-channel satellite imagery and NCEI [1] storm events. Then, it integrates and refines data in proper formats to be fed to the open-source Detectron [5] Deep learning software package from Facebook. The integration process involves validation on the respective data source as well as generating geospatial and temporal intersections. The project subsequently shifts to generating training datasets along with annotations to be ingested by Mask R-CNN [6] network architecture. Finally, it passes the generated training dataset as an input for Detectron [5] software application and attempts to train network for the given 2017 and 2018 storm events.
","abstract_html":"<p>Big Data has been playing a major role in the domain of Deep Learning applications as many companies and institutions continue to find solutions and extract certain trends in fields of climate change, weather forecasting and meteorology. This project extracts weather events data from multiple data sources that are supported by National Centers for Environmental information (NCEI) [1] and Amazon Web Services (AWS) [2]. Data sources include Next-Generation NEXRAD [3] Doppler radar reflectivity, GOES-16 [4] multi-channel satellite imagery and NCEI [1] storm events. Then, it integrates and refines data in proper formats to be fed to the open-source Detectron [5] Deep learning software package from Facebook. The integration process involves validation on the respective data source as well as generating geospatial and temporal intersections. The project subsequently shifts to generating training datasets along with annotations to be ingested by Mask R-CNN [6] network architecture. Finally, it passes the generated training dataset as an input for Detectron [5] software application and attempts to train network for the given 2017 and 2018 storm events.</p>","abstract_has_math":false,"creators":["alanbari, haidar A, Mr"],"institution":null,"degree_name":"Master of Science (M.S.)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Jianting Zhang","Akira Kawaguchi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-01T08:00:00Z","date_published":"2019-01-01T08:00:00Z","updated_at":"2026-07-24T01:57:21Z","subjects":["NEXRAD","GOES-16","NOAA","NCEI","AWS","Detectron","Data Storage Systems","Other Computer Engineering","Other Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/cc_etds_theses/887","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jianting Zhang","Akira Kawaguchi"]},{"key":"dc:creator","label":"Author","values":["alanbari, haidar A, Mr"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-05-24T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (M.S.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["NEXRAD","GOES-16","NOAA","NCEI","AWS","Detectron","Data Storage Systems","Other Computer Engineering","Other Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/cc_etds_theses/887"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Big Data has been playing a major role in the domain of Deep Learning applications as many companies and institutions continue to find solutions and extract certain trends in fields of climate change, weather forecasting and meteorology. This project extracts weather events data from multiple data sources that are supported by National Centers for Environmental information (NCEI) [1] and Amazon Web Services (AWS) [2]. Data sources include Next-Generation NEXRAD [3] Doppler radar reflectivity, GOES-16 [4] multi-channel satellite imagery and NCEI [1] storm events. Then, it integrates and refines data in proper formats to be fed to the open-source Detectron [5] Deep learning software package from Facebook. The integration process involves validation on the respective data source as well as generating geospatial and temporal intersections. The project subsequently shifts to generating training datasets along with annotations to be ingested by Mask R-CNN [6] network architecture. Finally, it passes the generated training dataset as an input for Detectron [5] software application and attempts to train network for the given 2017 and 2018 storm events.
"]},{"key":"dc:title","label":"Title","values":["Integrating Multi-Source Weather Data for Deep Learning"]}]}],"canonical_facts":{"dc:contributor":["Jianting Zhang","Akira Kawaguchi"],"dc:creator":["alanbari, haidar A, Mr"],"dc:date.available":["2019-05-24T07:00:00Z"],"dc:description.abstract":["Big Data has been playing a major role in the domain of Deep Learning applications as many companies and institutions continue to find solutions and extract certain trends in fields of climate change, weather forecasting and meteorology. This project extracts weather events data from multiple data sources that are supported by National Centers for Environmental information (NCEI) [1] and Amazon Web Services (AWS) [2]. Data sources include Next-Generation NEXRAD [3] Doppler radar reflectivity, GOES-16 [4] multi-channel satellite imagery and NCEI [1] storm events. Then, it integrates and refines data in proper formats to be fed to the open-source Detectron [5] Deep learning software package from Facebook. The integration process involves validation on the respective data source as well as generating geospatial and temporal intersections. The project subsequently shifts to generating training datasets along with annotations to be ingested by Mask R-CNN [6] network architecture. Finally, it passes the generated training dataset as an input for Detectron [5] software application and attempts to train network for the given 2017 and 2018 storm events.
"],"dc:identifier":["https://academicworks.cuny.edu/cc_etds_theses/887"],"dc:subject":["NEXRAD","GOES-16","NOAA","NCEI","AWS","Detectron","Data Storage Systems","Other Computer Engineering","Other Engineering"],"dc:title":["Integrating Multi-Source Weather Data for Deep Learning"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (M.S.)"]},"updated_at":"2026-07-24T01:57:21Z"}