Year: 2025
Type: conference-paper
Abstract: Crowdsensing is a promising method for collecting data related to wildland fires due to its combination of accuracy and ease of collection. This paper explores how crowdsensing data for prescribed fires may be collected and categorized to best suit the interests of various technical and general communities and presents a map-based web application dedicated to collecting crowdsensing fire data. The showcased web application allows users to input their own prescribed fire data directly to the database, from where it can be displayed along with other data points on a map and downloaded for personal use. The design of the data categories related to prescribed fires and their metadata is presented along with the implementation of the web application itself.
Source: ACM Southeast Conference (ACMSE)
Authors: Tony Imbesi, Xiaolin Hu, Ming Xin, Haiyang Chao
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W4410192406