Geospatial Applications in Wildlife Conservation and Management

Varghese, A. O.; Suryavanshi, Arun; Jha, Chandra Shekhar

doi:10.1007/978-3-030-98981-1_31

Water Science and Technology Library

2022

DOI: 10.1007/978-3-030-98981-1_31

|View full text |Cite

Geospatial Applications in Wildlife Conservation and Management

A. O. Varghese

Arun Suryavanshi²,

Chandra Shekhar Jha³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, it is necessary to collect more camera data from different scenarios to further improve the universality of the model. Fifth, smoke source localisation(Göltaş et al 2017;Varghese et al 2022) was not considered in the current model. When smoke is at different distances from the camera, there is variability in the smoke features in the image.…”

mentioning

confidence: 99%

FireFormer: an efficient Transformer to identify forest fire from surveillance cameras

Qiao

Wang

et al. 2023

Int. J. Wildland Fire

View full text Add to dashboard Cite

Background An effective identification model is crucial to realise the real-time monitoring and early warning of forest fires from surveillance cameras. However, existing models are prone to generate numerous false alarms under the interference of artificial smoke such as industrial smoke and villager cooking smoke, therefore a superior identification model is urgently needed. Aims In this study, we tested the Transformer-based model FireFormer to predict the risk probability of forest fire from the surveillance images. Methods FireFormer uses a shifted window self-attention module to extract similarities of divided patches in the image. The similarity in characteristics indicated the probability of forest fires. The GradCAM algorithm was then applied to analyse the interest area of FireFormer model and visualise the contribution of different image patches by calculating gradient reversely. To verify our model, the monitoring data from the high-point camera in Nandan Mountain, Foshan City, was collected and further constructed as a forest fire alarm dataset. Key results Our results showed that FireFormer achieved a competitive performance (OA: 82.21%, Recall: 86.635% and F1-score: 74.68%). Conclusions FireFormer proves to be superior to traditional methods. Implications FireFormer provides an efficient way to reduce false alarms and avoid heavy manual re-checking work.

show abstract

mentioning

confidence: 99%

FireFormer: an efficient Transformer to identify forest fire from surveillance cameras

Qiao

Wang

et al. 2023

Int. J. Wildland Fire

View full text Add to dashboard Cite

show abstract

Post-growth Human–Computer Interaction

Sharma,

Kumar,

Nardi

2023

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

Human–Computer Interaction (HCI) researchers have increasingly been questioning computing’s engagement with unsustainable and unjust economic growth, pushing for identifying alternatives. Incorporating degrowth, post-development, and steady-state approaches, post-growth philosophy offers an alternative not rooted in growth but in improving quality of life. It recommends an equitable reduction in resource use through sensible distributive practices where fulfillment is based on values including solidarity, cooperation, care, social justice, and localized development. In this paper, we describe opportunities for HCI to take a post-growth orientation in research, design, and practice to reimagine the design of sociotechnical systems toward advancing sustainable, just, and humane futures. We aim for the critiques, concerns, and recommendations offered by post-growth to be integrated into transformative HCI practices for technology-mediated change.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Geospatial Applications in Wildlife Conservation and Management

Cited by 2 publications

References 86 publications

FireFormer: an efficient Transformer to identify forest fire from surveillance cameras

FireFormer: an efficient Transformer to identify forest fire from surveillance cameras

Post-growth Human–Computer Interaction

Contact Info

Product

Resources

About