Spatio-temporal Data Analysis for Wildlife Conservation (GeoWildLife 2023).

About the Workshop

In collaboration with ACM SIGSPATIAL, we are pleased to announce the call for papers for GeoWildLife 2023, a workshop dedicated to bridging the gap between AI-enabled spatio-temporal data analytics and wildlife conservation.

The primary objective of this workshop is to advance the state-of-the-art in AI-driven spatio-temporal data analysis for wildlife conservation. By connecting computer scientists, geospatial scientists, ecologists, and conservation practitioners, the workshop seeks to promote interdisciplinary collaboration and drive real-world impact. Through a series of keynote presentations, panel discussions, and interactive sessions, participants will explore various topics including remote sensing technologies, predictive modeling, movement ecology, species distribution modeling, habitat quality assessment, and mitigating human-wildlife conflict. Special focus will be given to ethically and responsibly harnessing AI to ensure the sustainability of conservation efforts.

Accepted papers will be included in the workshop proceedings, which will be published in the ACM Digital Library. At least one author of each accepted paper must register for the workshop and present the paper. We also offer authors the option to opt out of the proceedings. Such papers will be published on the workshop’s website and will not be considered archival for resubmission purposes.

Call for Papers

Topics of interest include but are not limited to:

• Remote Sensing, UAV Imagery, and Wildlife Monitoring: AI interpretation of remote sensing data for tracking animal populations, identifying habitats, and monitoring ecosystem health. This includes techniques such as deep learning-based spatial data analysis.
• Predictive Modeling and Mobility Simulations for Conservation: AI-driven models to predict species movements, population changes, and the effects of environmental changes on wildlife, leveraging high-performance spatio-temporal mobility simulation systems.
• Movement Ecology, Urban Mobility, and AI: Using AI to understand species behavior and movement patterns based on spatial and temporal data in both natural and urban environments.
• Species Distribution and Semantic Trajectory Modeling: Applications of AI in predicting and mapping the spatial distribution of different species under changing climate scenarios and leveraging generative models for semantic trajectory analysis.
• AI in Habitat Quality Assessment and Urban Landscape Analysis: Employing AI algorithms to analyze, interpret spatial data, and evaluate urban habitats using augmented street-level imagery and points of interest.
• Mitigating Human-Wildlife and Human-Urban Conflicts: Developing AI tools to predict and prevent conflicts between humans and wildlife and understanding spatio-temporal patterns in urban habitats.
• Spatio-temporal Data Analytics for Movement Ecology, Wildlife Monitoring, and Natural Disaster Prediction: Integrating multi-output neural networks to predict natural phenomena like earthquakes, drought that could impact wildlife habitats.
• Conservation Planning, Decision Support Systems, and Urban Infrastructure Mapping: Using AI to aid in spatial planning for conservation, identifying priority areas for protection, and mapping urban structures that might impact wildlife movement.
• AI, Ethics, Data Privacy, and Wildlife Conservation: Delving into the ethical implications of using AI in wildlife conservation, including concerns related to privacy, surveillance, data ownership, and location-based biases.
• Socio-Political Implications of AI in Conservation and Urban Mobility: Understanding the socio-political dimensions of AI-based conservation strategies and their impact on urban landscapes and communities.
• AI-driven Citizen Science, Crowd-sourced Data: Leveraging AI-enhanced citizen science approaches in wildlife monitoring, conservation efforts, and understanding spatio-temporal patterns through narrative techniques.
• Urban Representation Learning and Urban Mobility Analysis in Conservation: Applying graph neural networks and deep learning techniques to understand urban economic statuses and their implications for wildlife movement.
• Distributed Data Warehousing and Indexing for Wildlife Conservation: Techniques like efficient storage and indexing for handling massive datasets related to wildlife monitoring and urban landscapes.

Submission guidelines

We welcome different kinds of papers, such as, but not limited to:

• Novel research papers
• Demo papers
• Dataset and Challenge papers
• Work-in-progress papers
• Visionary papers (white papers)
• Review paper (Relevant work that has been previously published)

Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions. We also encourage authors to submit papers based on their previously submitted or recently published work (within the last year) for consideration in the poster session. This provides an opportunity to familiarize the community with noteworthy research that may not be entirely new and subsequently brainstorm new ideas.

All papers will be peer reviewed, single-blinded. Submissions must be in PDF, no more than 8 pages (long/ full) or 4 pages (short) or 2 pages (abstract/ poster) — and formatted according to the ACM camera-ready templates available at https://www.acm.org/publications/proceedings-template

Authors whose papers are accepted to the workshop will have the opportunity to participate in a spotlight and poster session, and some set will also be chosen for oral presentation.

Important Dates

• Paper submission deadline: August 30, 2023 (AoE) September 26, 2023 (AoE)
• Notification of acceptance: October 10, 2023
• Camera-ready paper deadline: October 20, 2023 (AoE)
• Workshop date: November 13, 2023

Submission Link!

Keynote Speakers

Coming soon...

Accepted Papers and Schedule

Will be available here

Organizers