Work

Consensus-Enabled Active Learning for Multi-Agent Systems in Ground Classification of Wetlands

Year: 2024

Type: article

Source: Journal of Aerospace Information Systems

DOI: 10.2514/1.i011337

Authors: Chandima Fernando, Elizabeth Basha, Justin Bradley, Carrick Detweiler

Institutions: University of Nebraska–Lincoln, University of the Pacific

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Science Foundation, National Aeronautics and Space Administration

Awards: NRI: INT: COLLAB: Leveraging Environmental Monitoring UAS in Rainforests, G1838650217, CAREER: Foundations for a Resource-Aware, Cyber-Physical Vehicle Autonomy, NRI: INT: COLLAB: Raining Drones: Mid-Air Release & Recovery of Atmospheric Sensing Systems, G5962858536

OpenAlex ID: W4405797090

FWCI: 0.152

Cites: 12

Cited by: 1

Related to: 10