9:00-9:10 AM

Opening remarks by InfoWild'23 Workshop Organizers

9:10-10:30 AM

Keynote talk by Dr. Scott Pezanowski

President, BrightWorld Labs

Topic: What do scientists researching wildlife conservation have in common with realtors?

Abstract: We cannot emphasize enough the value of location in wildlife conservation. Location can be explicit when you use GPS and other location tagging to track wildlife. And location can be implicit when the public, media, and policymakers talk about places and other things we can tie to location. The scientific community has produced valuable insights by analyzing the movement of people, wildlife, goods, and more, where the data is precise movement trajectories. However, research should focus more on geographic movement from text descriptions. Descriptions of things moving include rich contextual information that describes what is moving, when, why, and how it moves. There are many challenges to utilizing this source, like location ambiguities and fusion with other data sources. But, combining Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning with visual analytics will help us better utilize this source to monitor and analyze wildlife for conservation. Finally, my most important messages from this talk will be 1) geospatial analysis is vital to all key topics in knowledge extraction and management for wildlife conservation and 2) the geospatial industry unnecessarily complicates geospatial analysis, and many scientists outside the field would benefit from knowing it is simply not that unique.

10:30-11:00 AM

Break

11:00-11:20 AM

Paper Presentation: 20min, QA: 10min

Title: A Flexible and Scalable Approach for Collecting Wildlife Advertisements on the Web

Authors: Juliana Barbosa, Juliana Freire and Sunandan Chakraborty

Abstract: Wildlife traffickers are increasingly carrying out their activities in cyberspace. As they advertise and sell wildlife products in online marketplaces, they leave digital traces of their activity. This creates a new opportunity: by analyzing these traces, we can obtain insights into how trafficking networks work as well as how they can be disrupted. However, collecting such information is difficult. Online marketplaces sell a very large number of products and identifying ads that actually involve wildlife is a complex task that is hard to automate. Furthermore, given that the volume of data is staggering, we need scalable mechanisms to acquire, filter, and store the ads, as well as to make them available for analysis. In this paper, we present a new approach to collect wildlife trafficking data at scale. We propose a data collection pipeline that combines scoped crawlers for data discovery and acquisition with foundational models and machine learning classifiers to identify relevant ads. We describe a dataset we created using this pipeline which is, to the best of our knowledge, the largest of its kind: it contains almost a million ads obtained from 41 marketplaces, covering 235 species and 20 languages.

11:30 AM-12:30 PM

Talk and Discussion on the topic

Analysis of Elephant Movement in Sub-Saharan Africa: Ecological, Climatic, and Conservation Perspectives

Authors: Matthew Hines, Gregory Glatzer,Shreya Ghosh and Prasenjit Mitra. ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS '23)

Abstract: The interaction between elephants and their environment has profound implications for both ecology and conservation strategies. This study presents an analytical approach to decipher the intricate patterns of elephant movement in Sub-Saharan Africa, concentrating on key ecological drivers such as seasonal variations and rainfall patterns. Despite the complexities surrounding these influential factors, our analysis provides a holistic view of elephant migratory behavior in the context of the dynamic African landscape. Our comprehensive approach enables us to predict the potential impact of these ecological determinants on elephant migration, a critical step in establishing informed conservation strategies. This projection is particularly crucial given the impacts of global climate change on seasonal and rainfall patterns, which could substantially influence elephant movements in the future. The findings of our work aim to not only advance the understanding of movement ecology but also foster a sustainable coexistence of humans and elephants in Sub-Saharan Africa. By predicting potential elephant routes, our work can inform strategies to minimize human-elephant conflict, effectively manage land use, and enhance anti-poaching efforts. This research underscores the importance of integrating movement ecology and climatic variables for effective wildlife management and conservation planning.

12:30-1:30 PM

Lunch Break

1:30-2:30 PM

Keynote talk by Dr. Thomas Muller

Goethe University and Senckenberg Biodiversity and Climate Research Centre, Frankfurt (Germany)

Topic: Animal Movement and Conservation

2:30-2:50 PM

Paper Presentation: 20min, QA: 10min

Title: WildGEN: Long-horizon Trajectory Generation for Wildlife

Authors: Ali Al-Lawati, Elsayed Eshra and Prasenjit Mitra

Abstract: Trajectory generation is an important concern in pedestrian, vehicle, and wildlife movement studies. Generated trajectories help enrich the training corpus in relation to deep learning applications, and may be used to facilitate simulation tasks. This is especially significant in the wildlife domain, where the cost of obtaining addi- tional real data can be prohibitively expensive, time-consuming, and bear ethical considerations. In this paper, we introduce WildGEN: a conceptual framework that addresses this challenge by employing a Variational Auto-encoders (VAEs) based method for the acquisition of movement characteristics exhibited by wild geese over a long horizon using a sparse set of truth samples. A subsequent post- processing step of the generated trajectories is performed based on smoothing filters to reduce excessive wandering. Our evaluation is conducted through visual inspection and the computation of the Hausdorff distance between the generated and real trajectories. In addition, we utilize the Pearson Correlation Coefficient as a way to measure how realistic the trajectories are based on the similarity of clusters evaluated on the generated and real trajectories.

3:00-3:30 PM

Break

3:30-4:15 PM

Keynote talk by Dr. Bistra Dilkina

Dr. Allen and Charlotte Ginsburg Early Career Chair in Computer Science and Associate Professor of Computer Science, University of Southern California.

Topic: AI for Wildlife Conservation

4:15-5:00 PM

Keynote talk by Christopher Yeh

Computing and Mathematical Sciences, California Institute of Technology, USA

Topic: Shared Challenges across Machine Learning for Sustainability

Abstract: Machine learning techniques have demonstrated significant advancements across a wide range of sustainability issues, ranging from poverty mapping to energy systems and wildlife conservation. While the domains are distinct, the underlying machine learning problems have a number of shared challenges modeling challenges. In this talk, I will describe how the problems of datasets curation, uncertainty quantification, decision-focused learning, and active learning are both shared and intertwined. By understanding and addressing these shared challenges, we can maximize the potential of machine learning in tackling pressing global sustainability issues.

5:00-5:30 PM

Panel Discussion and Concluding Remarks

Discussion Topic: "What is the next step?"

Concluding Remarks by Dr. Prasenjit Mitra

Future directions in the field of AI-driven wildlife conservation: (1) Can we discern and interpret spatio-temporal patterns in largescale human activity data related to the frequency and intensity of human-wildlife interactions and conflicts? (2) How can we quantify and predict the impact of various sound and light pollution types on wildlife behavior and visitor experiences in protected areas? (3) To what extent do human-induced habitat modifications, such as land-use changes and settlements, impact the signature mobility or activity patterns of wildlife? Can we develop an AI model to quantify these effects? (4) How can we leverage AI-driven spatio-temporal data analysis to enhance protection strategies for endangered species? (5) Can we utilize spatio-temporal data analysis to predict wildlife-human conflicts and devise risk mitigation strategies? (6) What are the potential obstacles and limitations when employing AI for spatio-temporal data analysis in wildlife conservation? (7) How can we apply AI-driven spatio-temporal analysis to understand the effects of climate change on wildlife migration patterns? (8) Can AI-driven spatio-temporal data analysis aid in disease surveillance within wildlife populations? (9) How do we ensure ethical use of AI and uphold animal privacy rights in wildlife conservation?

PROGRAM COMMITTEE

• Sanjay Chawla, Research Director of QCRI’s Data Analytics department
• Dan Morris, Research Scientist, Google AI for Nature and Society
• Bing Pan, Professor of Commercial Recreation and Tourism, The Pennsylvania State University
• Lily Xu, Ph.D. Student, Harvard University, USA
• Saptarshi Sengupta, Ph.D. Student, College of IST, Pennsylvania State University, USA
• Fei Fang, Assistant Professor of Computer Science at Carnegie Mellon University, Pennsylvania
• Emmanuel Dufourq, AIMS-Canada Junior Research Chair
• Johnson Kinyua, Associate Teaching Professor, College of IST, Pennsylvania State University, USA
• Brendan Derrick Taff, Associate Professor, Recreation, Park, and Tourism Management, The Pennsylvania State University, USA
• Edwin Sabuhoro, Assistant Professor,Recreation, Park, and Tourism Management, The Pennsylvania State University, USA
• Gileard Minja, Mwenge Catholic University Tanzania
• Derek Lee, The Pennsylvania State University, USA
• Andrew Perrault, Assistant Professor, The Ohio State University, USA
• Titus Enock Adhola, Lecturer, University of Nairobi, Kenya
• Randall Boone, Assistant Professor, Colorado State University, USA
• Lorène Jeantet, Postdoctoral Researcher, University of Stellenbosch, African Institute for Mathematical Sciences - AIMS South Africa